diff --git a/.github/workflows/precommit.yml b/.github/workflows/precommit.yml new file mode 100644 index 0000000..f37a3a7 --- /dev/null +++ b/.github/workflows/precommit.yml @@ -0,0 +1,41 @@ +name: Precommit + +on: + push: + branches: [main] + pull_request: + branches: [main] + +permissions: + contents: read + +jobs: + precommit: + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v6 + + - uses: prefix-dev/setup-pixi@v0.9.3 + with: + pixi-version: latest + cache: true + + # ── Stubs cache ──────────────────────────────────────────────── + - name: Restore typings cache + uses: actions/cache@v5 + id: typings-cache + with: + path: typings + key: typings-${{ hashFiles('pyproject.toml') }} + + - name: Generate stubs if cache miss + if: steps.typings-cache.outputs.cache-hit != 'true' + run: pixi run pybind11-stubgen mujoco -o typings/ + + # ── Pre-commit (ruff lint, ruff format, trailing whitespace, etc.) + - name: Run pre-commit + run: pixi run pre-commit run --all-files + + # ── Type checking ────────────────────────────────────────────── + - name: Run Pyright + run: pixi run pyright sumo/ diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..a8082ec --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,48 @@ +name: Test + +on: + push: + branches: [main] + pull_request: + branches: [main] + +permissions: + contents: read + +jobs: + test: + runs-on: ubuntu-22.04 + + steps: + - uses: actions/checkout@v6 + + - uses: prefix-dev/setup-pixi@v0.9.3 + with: + pixi-version: latest + cache: true + + # ── Build C++ extensions ────────────────────────────────────── + - name: Restore build cache + id: ext-cache + uses: actions/cache@v5 + with: + path: | + g1_extensions/build + key: build-${{ hashFiles('pixi.lock', 'g1_extensions/**/*.cpp', 'g1_extensions/**/*.h', 'g1_extensions/**/CMakeLists.txt') }} + + - name: Build extensions + run: pixi run build + + # ── Download mesh assets ────────────────────────────────────── + - name: Download meshes from release + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: pixi run python -c "from judo.utils.assets import download_and_extract_meshes; from judo import MODEL_PATH; download_and_extract_meshes(str(MODEL_PATH))" + + # ── Pre-warm robot_descriptions cache ───────────────────────── + - name: Cache mujoco_menagerie meshes + run: pixi run python -c "from robot_descriptions import spot_mj_description" + + # ── Tests ───────────────────────────────────────────────────── + - name: Run tests + run: pixi run pytest -rsx diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..60c7718 --- /dev/null +++ b/.gitignore @@ -0,0 +1,220 @@ +# macOS +.DS_Store + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock +#poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +#pdm.lock +#pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +#pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Cursor +# Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to +# exclude from AI features like autocomplete and code analysis. Recommended for sensitive data +# refer to https://docs.cursor.com/context/ignore-files +.cursorignore +.cursorindexingignore + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# judo source checkout for building mujoco_extensions +.judo-src/ + +out/ +outputs/ + +# typings +# see: https://github.com/google-deepmind/mujoco/issues/1292 +typings/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..1322e49 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,17 @@ +default_language_version: + python: python3 +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer +- repo: https://github.com/charliermarsh/ruff-pre-commit + rev: v0.11.8 + hooks: + - id: ruff + types_or: [ python, pyi, jupyter ] + args: [ --fix, --config=pyproject.toml ] + - id: ruff-format + types_or: [ python, pyi, jupyter ] + args: [ --config=pyproject.toml ] diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..981e4ac --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,26 @@ +# Repository Guidelines + +## Project Structure & Module Organization +`sumo/` is the active package. Put task code in `sumo/tasks/g1/` or `sumo/tasks/spot/`, controller wrappers in `sumo/controller/`, CLI and MPC entrypoints in `sumo/cli.py` and `sumo/run_mpc/`, and MuJoCo XML/mesh assets under `sumo/models/`. `g1_extensions/` contains the optional pybind11 extension for G1 backends. `tests/` holds the pytest suite. Treat `judo-private/` as an old fork used only as migration reference; match new code to the public `judo-rai` repo instead. Use `.judo-src/` as the local reference checkout when porting behavior. + +## Build, Test, and Development Commands +Use `pixi` by default: + +```bash +pixi install +pixi run build +pixi run build-judo-ext +pixi run pytest tests/ -v +pixi run sumo --init-task g1_box --num-episodes 1 +``` + +`pixi run build` compiles `g1_extensions`; `pixi run build-judo-ext` builds Judo's `mujoco_extensions` backend for Spot tasks. `pixi install -e dev` remains equivalent to `pixi install` for this repo, but `pixi run` without `-e` already targets the default full app environment. Use `pip install -e .` only as a lightweight fallback when you do not need the managed `pixi` environment. + +## Coding Style & Naming Conventions +Write Python with 4-space indentation, explicit imports, and type-aware dataclass configs. Follow existing naming: `snake_case` for modules/functions, `PascalCase` for classes, and `*Config` for task/config dataclasses such as `G1BoxConfig`. New task modules should follow patterns like `g1_box.py` or `spot_table_drag.py`. Prefer `ruff check .` before submitting changes. When porting code, preserve `judo-rai` APIs and conventions first, even if `judo-private/` differs. + +## Testing Guidelines +Add tests in `tests/` and name files `test_*.py`. Cover imports, task registration, and task-specific behavior such as `reset()`, `reward()`, and control shape. Mark extension-only tests with `@pytest.mark.g1_extensions`; `tests/conftest.py` skips them when `g1_extensions` is unavailable. When adding a task, update registration in `sumo/tasks/__init__.py` and add at least one smoke test. + +## Commit & Pull Request Guidelines +The repo history is minimal, so use short imperative commit subjects. Scoped messages are preferred, for example `feat(tasks): port spot_table_drag`. PRs should summarize the port or feature, note what was aligned to public `judo-rai`, list commands run, and include screenshots or logs for simulator-facing changes. Avoid mixing migration cleanup with unrelated refactors. diff --git a/README.md b/README.md index 1d23abe..a23d0fa 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,78 @@ -# Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation -`sumo` is a `python` package that implements a combination of hierarchical RL and sampling-based MPC to execute dynamic whole-body tasks. +# Sumo 🥋 + +`sumo`🥋 is a research codebase for whole-body loco-manipulation built on +[`judo`🥋](https://github.com/bdaiinstitute/judo). + +## Install + +Requires [pixi](https://pixi.sh). + +```bash +# Install the default app environment +pixi install + +# Build the local g1_extensions extension (required for G1 tasks) +# and judo's mujoco_extensions (required for Spot tasks) +pixi run build + +``` + +`pixi run sumo` assumes both native extensions above have already been built. + +If you prefer named environments, `pixi install -e dev` is equivalent for this repo, +and the corresponding task form is `pixi run -e dev ...`. + +## Run + +```bash +# Launch simulation + visualizer with default task +pixi run sumo + +# Specify a task +pixi run sumo task=spot_box_push + +# Specify a task and other hydra options (e.g. optimizer) +pixi run sumo task=g1_box optimizer=mppi +``` + +## Headless MPC Runner + +`run_mpc` runs MPC episodes without the full GUI, useful for benchmarking and data collection. + +```bash +# Run with defaults (spot_box_push, CEM optimizer, 2 episodes) +pixi run python -m sumo.run_mpc + +# Specify task and options +pixi run python -m sumo.run_mpc --init-task=g1_door --init-optimizer=cem --num-episodes=10 + +# With visualization +pixi run python -m sumo.run_mpc --init-task=g1_box --visualize + +# Disable saving results +pixi run python -m sumo.run_mpc --init-task=spot_box_push --no-save-results + +# Record all rollout data +pixi run python -m sumo.run_mpc --init-task=g1_door --record-all-data +``` + +Results are saved to `run_mpc/results/` by default as HDF5 files. Use `--help` to see all options. + +## Tests + +```bash +pixi run pytest tests/ -v +``` + +## Reference + +If you find this codebase helpful, please consider citing the SUMO paper. + +```bibtex +@article{zhang2026sumo, + title={Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation}, + author={John Z. Zhang and Maks Sorokin and Jan Brudigam and Brandon Hung and Stephen Phillips and Dmitry Yershov and Farzad Niroui and Tong Zhao and Leonor Fermoselle and Xinghao Zhu and Chao Cao and Duy Ta and Tao Pang and Jiuguang Wang and Preston Culbertson and Zachary Manchester and Simon Le Cleac'h}, + year={2026}, + note={TODO: add arXiv link} +} +``` diff --git a/g1_extensions/CMakeLists.txt b/g1_extensions/CMakeLists.txt new file mode 100644 index 0000000..ac51be0 --- /dev/null +++ b/g1_extensions/CMakeLists.txt @@ -0,0 +1,105 @@ +cmake_minimum_required(VERSION 3.15) +project(g1_extensions) + +set(CMAKE_CXX_STANDARD 17) +find_package(OpenMP REQUIRED) +find_package(Eigen3 REQUIRED) + +# 1. Locate Python, which is used to find the MuJoCo package +find_package(Python + COMPONENTS + Interpreter + Development.Module + REQUIRED +) +execute_process( + COMMAND ${Python_EXECUTABLE} -c + "import mujoco, os; print(os.path.dirname(mujoco.__file__))" + OUTPUT_VARIABLE MJ_PKG_ROOT + OUTPUT_STRIP_TRAILING_WHITESPACE +) +message(STATUS "MuJoCo package root: ${MJ_PKG_ROOT}") + +# 2. Glob for the actual libmujoco.so file(s) there +file(GLOB MUJOCO_LIB_FILES + "${MJ_PKG_ROOT}/libmujoco.so*" # Linux + "${MJ_PKG_ROOT}/libmujoco.dylib" # macOS default + "${MJ_PKG_ROOT}/libmujoco.*.dylib" # macOS versioned dylib +) +if(NOT MUJOCO_LIB_FILES) + message(FATAL_ERROR "Could not find any libmujoco.so* in ${MJ_PKG_ROOT}") +endif() +list(GET MUJOCO_LIB_FILES 0 MUJOCO_LIB) +message(STATUS "Linking against MuJoCo library: ${MUJOCO_LIB}") + +# 3. Pull in mujoco headers +set(MJINC_DIR "${MJ_PKG_ROOT}/include") +message(STATUS "MuJoCo include dir: ${MJINC_DIR}") +include_directories(${MJINC_DIR}) + +# 4. Build the pybind11 module +find_package(pybind11 REQUIRED) +pybind11_add_module(_g1_extensions + bindings.cpp + rollout.cpp + g1_rollout.cpp +) +execute_process( + COMMAND ${Python_EXECUTABLE} -c "import site; print(site.getsitepackages()[0])" + OUTPUT_VARIABLE SITE_PACKAGES_DIR + OUTPUT_STRIP_TRAILING_WHITESPACE +) +set_target_properties(_g1_extensions PROPERTIES + LIBRARY_OUTPUT_DIRECTORY "${SITE_PACKAGES_DIR}/g1_extensions" +) + +# Configure RPATHs correctly for each platform +if(APPLE) + set_target_properties(_g1_extensions PROPERTIES + MACOSX_RPATH ON + BUILD_WITH_INSTALL_RPATH TRUE + BUILD_RPATH "@loader_path/../mujoco" + INSTALL_RPATH "@loader_path/../mujoco" + ) + get_filename_component(MUJOCO_LIB_NAME "${MUJOCO_LIB}" NAME) + add_custom_command(TARGET _g1_extensions POST_BUILD + COMMAND install_name_tool -change + "@rpath/mujoco.framework/Versions/A/${MUJOCO_LIB_NAME}" + "@rpath/${MUJOCO_LIB_NAME}" + "$" + VERBATIM) +else() + set_target_properties(_g1_extensions PROPERTIES + BUILD_RPATH "$ORIGIN/../mujoco" + INSTALL_RPATH "$ORIGIN/../mujoco" + BUILD_RPATH_USE_ORIGIN TRUE + ) +endif() +target_link_libraries(_g1_extensions PRIVATE + Eigen3::Eigen + OpenMP::OpenMP_CXX + ${MUJOCO_LIB} +) + +# 6. Link ONNX Runtime from the current environment (expects conda/pixi env) +if(DEFINED ENV{CONDA_PREFIX}) + set(CONDA_PREFIX $ENV{CONDA_PREFIX}) + include_directories("${CONDA_PREFIX}/include") + file(GLOB ONNXRUNTIME_LIB_FILES + "${CONDA_PREFIX}/lib/libonnxruntime.*" # Linux/macOS + "${CONDA_PREFIX}/Library/lib/onnxruntime.lib" # Windows import lib + "${CONDA_PREFIX}/Library/bin/onnxruntime.dll" # Windows DLL + ) + if(ONNXRUNTIME_LIB_FILES) + list(GET ONNXRUNTIME_LIB_FILES 0 ONNXRUNTIME_LIB) + message(STATUS "Linking against ONNX Runtime: ${ONNXRUNTIME_LIB}") + target_link_libraries(_g1_extensions PRIVATE ${ONNXRUNTIME_LIB}) + else() + message(WARNING "Could not find ONNX Runtime library under ${CONDA_PREFIX}. Ensure onnxruntime is installed.") + endif() +else() + message(WARNING "CONDA_PREFIX not set; skipping ONNX Runtime linking.") +endif() + +# 5. Install the module +install(TARGETS _g1_extensions DESTINATION g1_extensions) diff --git a/g1_extensions/__init__.py b/g1_extensions/__init__.py new file mode 100644 index 0000000..a1b9b37 --- /dev/null +++ b/g1_extensions/__init__.py @@ -0,0 +1,7 @@ +import os + +_PACKAGE_DIR = os.path.dirname(os.path.abspath(__file__)) +POLICY_DIR = os.path.join(_PACKAGE_DIR, "policy") +os.environ.setdefault("G1_EXTENSIONS_POLICY_DIR", POLICY_DIR) + +from g1_extensions._g1_extensions import G1Rollout, rollout, sim, sim_g1 diff --git a/g1_extensions/bindings.cpp b/g1_extensions/bindings.cpp new file mode 100644 index 0000000..5d68ca1 --- /dev/null +++ b/g1_extensions/bindings.cpp @@ -0,0 +1,176 @@ +#include +#include +#include +#include + +#include "rollout.h" +#include "g1_rollout.h" + +namespace py = pybind11; + +static std::vector getModelVector(const py::list& python_models) { + std::vector model_vector; + model_vector.reserve(python_models.size()); + for (auto&& item : python_models) { + auto ptr = item.attr("_address").cast(); + model_vector.push_back(reinterpret_cast(ptr)); + } + return model_vector; +} + +static std::vector getDataVector(const py::list& python_data) { + std::vector data_vector; + data_vector.reserve(python_data.size()); + for (auto&& item : python_data) { + auto ptr = item.attr("_address").cast(); + data_vector.push_back(reinterpret_cast(ptr)); + } + return data_vector; +} + +PYBIND11_MODULE(_g1_extensions, m) { + // Function to shutdown persistent thread pool + m.def("shutdown_thread_pool", + []() { + ThreadPoolManager::instance().shutdown(); + }, + R"doc( +Shutdown the persistent thread pool. + +Call this function to clean up the persistent thread pool when done with rollouts. +The pool will be automatically recreated on the next call to persistent_cpp_rollout. +)doc"); + + // Rollout + m.def("rollout", + [](const py::list& models, + const py::list& data, + const py::array_t& x0, + const py::array_t& controls) + { + auto models_cpp = getModelVector(models); + auto data_cpp = getDataVector(data); + return Rollout(models_cpp, data_cpp, x0, controls); + }, + py::arg("models"), + py::arg("data"), + py::arg("x0"), + py::arg("controls"), + R"doc( +Run parallel MuJoCo rollouts. + + Args: + models: length-B list of mujoco._structs.MjModel + data: length-B list of mujoco._structs.MjData + x0: 2D array of shape (B, nq+nv), batched initial [qpos;qvel] + controls: 3D array of shape (B, horizon, nu), batched control inputs + +Returns: + tuple of three np.ndarray: + states -> shape (B, horizon+1, nq+nv) - MuJoCo states (includes initial state) + sensors -> shape (B, horizon, nsensordata) - sensor data +)doc"); + + // Sim + m.def("sim", + [](py::object model, + py::object data, + const py::array_t& x0, + const py::array_t& controls) + { + auto model_ptr = reinterpret_cast(model.attr("_address").cast()); + auto data_ptr = reinterpret_cast(data.attr("_address").cast()); + Sim(model_ptr, data_ptr, x0, controls); + }, + py::arg("model"), + py::arg("data"), + py::arg("x0"), + py::arg("controls"), + R"doc( +Run a single MuJoCo simulation step. + + Args: + model: mujoco._structs.MjModel + data: mujoco._structs.MjData + x0: 1D array of shape (nq+nv), initial [qpos;qvel] + controls: 1D array of shape (nu), control input + +Returns: + None +)doc"); + + // G1 Sim + m.def("sim_g1", + [](py::object model, + py::object data, + const py::array_t& x0, + const py::array_t& command, + const py::array_t& prev_policy) + { + auto model_ptr = reinterpret_cast(model.attr("_address").cast()); + auto data_ptr = reinterpret_cast(data.attr("_address").cast()); + return SimG1(model_ptr, data_ptr, x0, command, prev_policy); + }, + py::arg("model"), + py::arg("data"), + py::arg("x0"), + py::arg("command"), + py::arg("prev_policy"), + R"doc( +Run a single MuJoCo step with G1 ONNX policy to generate controls. + +Args: + model: mujoco._structs.MjModel + data: mujoco._structs.MjData + x0: 1D array of shape (nq+nv), initial [qpos;qvel] + command: 1D array of shape (17), command [vx, vy, wz, left_arm(7), right_arm(7)] + prev_policy: 1D array of shape (29), previous policy output + +Returns: + 1D array of shape (29): New policy output for next iteration +)doc"); + + // G1Rollout class - mimicking mujoco.rollout.Rollout API + py::class_(m, "G1Rollout") + .def(py::init(), py::arg("nthread") = 0, py::arg("cutoff_time") = 0.2, + R"doc( +Create a G1Rollout object with thread pool for parallel rollouts. + +Args: + nthread: Number of threads in pool. If 0, runs single-threaded. + cutoff_time: Maximum time allowed per rollout in seconds (default 0.2s). +)doc") + .def("rollout", + [](G1Rollout& self, + const py::list& models, + const py::list& data, + const py::array_t& initial_state, + const py::array_t& controls) + { + auto models_cpp = getModelVector(models); + auto data_cpp = getDataVector(data); + return self.rollout(models_cpp, data_cpp, initial_state, controls); + }, + py::arg("models"), + py::arg("data"), + py::arg("initial_state"), + py::arg("controls"), + R"doc( +Perform parallel rollouts with G1 ONNX policy. + +Args: + models: List of mujoco._structs.MjModel instances + data: List of mujoco._structs.MjData instances + initial_state: Array of shape (nbatch, nstate) with initial states + controls: Array of shape (nbatch, nsteps, 17) with command sequences + +Returns: + Tuple of (states, sensordata) arrays matching mujoco.rollout API +)doc") + .def("close", &G1Rollout::close, + R"doc(Close the rollout object and cleanup resources.)doc") + .def("__enter__", &G1Rollout::__enter__, py::return_value_policy::reference_internal) + .def("__exit__", &G1Rollout::__exit__) + .def_property_readonly("nthread", &G1Rollout::get_num_threads, + R"doc(Number of threads in the thread pool.)doc"); +} diff --git a/g1_extensions/g1_rollout.cpp b/g1_extensions/g1_rollout.cpp new file mode 100644 index 0000000..912b57e --- /dev/null +++ b/g1_extensions/g1_rollout.cpp @@ -0,0 +1,738 @@ +#include "g1_rollout.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; + +// ONNX Policy wrapper class +class OnnxPolicy { +public: + explicit OnnxPolicy(const std::shared_ptr& session) + : session_(session), memory_info_(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU)) { + Ort::Allocator allocator(*session_, memory_info_); + input_name_ = session_->GetInputNameAllocated(0, allocator).get(); + output_name_ = session_->GetOutputNameAllocated(0, allocator).get(); + input_shape_ = session_->GetInputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape(); + output_shape_ = session_->GetOutputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape(); + input_size_ = static_cast(input_shape_[1]); + output_size_ = static_cast(output_shape_[1]); + } + + std::vector run(const std::vector& observation) { + if ((int)observation.size() != input_size_) { + throw std::runtime_error("Observation size does not match ONNX input dimension"); + } + std::array ishape = { 1, static_cast(observation.size()) }; + Ort::Value input_tensor = Ort::Value::CreateTensor( + memory_info_, const_cast(observation.data()), observation.size(), ishape.data(), 2); + const char* in_names[1] = { input_name_.c_str() }; + const char* out_names[1] = { output_name_.c_str() }; + auto outputs = session_->Run(run_options_, in_names, &input_tensor, 1, out_names, 1); + auto& out = outputs[0]; + float* ptr = out.GetTensorMutableData(); + auto info = out.GetTensorTypeAndShapeInfo(); + size_t n = info.GetElementCount(); + return std::vector(ptr, ptr + n); + } + + int input_size() const { return input_size_; } + int output_size() const { return output_size_; } + +private: + std::shared_ptr session_; + Ort::MemoryInfo memory_info_; + Ort::RunOptions run_options_; + std::string input_name_; + std::string output_name_; + std::vector input_shape_; + std::vector output_shape_; + int input_size_ = 0; + int output_size_ = 0; +}; + +// Utility functions +py::array_t make_array_owned_g1(std::vector& buf, int B, int T, int D) { + std::vector shape = { B, T, D }; + std::vector strides = { + static_cast(sizeof(double) * T * D), + static_cast(sizeof(double) * D), + static_cast(sizeof(double)) + }; + auto heap_buf = new std::vector(std::move(buf)); + py::capsule free_when_done(heap_buf, [](void *p) { delete reinterpret_cast*>(p); }); + return py::array_t(shape, strides, heap_buf->data(), free_when_done); +} + +// ONNX session allocator +static std::shared_ptr allocate_shared_session(const std::string& onnx_path) { + static Ort::Env env; + Ort::SessionOptions opts; + opts.SetIntraOpNumThreads(1); + opts.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_BASIC); + return std::make_shared(env, onnx_path.c_str(), opts); +} + +// Metadata structure for G1 policy +struct G1Metadata { + int num_joints; + int obs_dim; + std::vector default_joint_pos; + std::vector action_scale; + std::vector joint_names; + std::vector arm_joint_indices; + + G1Metadata() : num_joints(29), obs_dim(99) {} +}; + +// Global metadata storage +static G1Metadata g_g1_metadata; +static bool g_g1_metadata_loaded = false; + +// Helper function to parse comma-separated values +static std::vector parse_csv_floats(const std::string& csv) { + std::vector result; + std::stringstream ss(csv); + std::string item; + while (std::getline(ss, item, ',')) { + result.push_back(std::stod(item)); + } + return result; +} + +static std::vector parse_csv_strings(const std::string& csv) { + std::vector result; + std::stringstream ss(csv); + std::string item; + while (std::getline(ss, item, ',')) { + result.push_back(item); + } + return result; +} + +// Parse metadata from ONNX model using ONNX Runtime session +static void parse_g1_metadata(const std::shared_ptr& session) { + if (g_g1_metadata_loaded) return; + + try { + // Get model metadata from session + Ort::AllocatorWithDefaultOptions allocator; + Ort::ModelMetadata metadata = session->GetModelMetadata(); + + // Parse metadata properties + std::unordered_map metadata_map; + + // Try to get custom metadata keys + std::vector keys_to_try = { + "default_joint_pos", "action_scale", "joint_names" + }; + + for (const auto& key : keys_to_try) { + try { + Ort::AllocatedStringPtr value_ptr = metadata.LookupCustomMetadataMapAllocated(key.c_str(), allocator); + if (value_ptr) { + metadata_map[key] = std::string(value_ptr.get()); + } + } catch (...) { + // Key not found, skip + } + } + + // Parse default_joint_pos + if (metadata_map.count("default_joint_pos")) { + g_g1_metadata.default_joint_pos = parse_csv_floats(metadata_map["default_joint_pos"]); + g_g1_metadata.num_joints = static_cast(g_g1_metadata.default_joint_pos.size()); + } else { + // Fallback to zeros + g_g1_metadata.default_joint_pos.assign(29, 0.0); + } + + // Parse action_scale + if (metadata_map.count("action_scale")) { + g_g1_metadata.action_scale = parse_csv_floats(metadata_map["action_scale"]); + } else { + // Fallback to 0.25 for all joints + g_g1_metadata.action_scale.assign(g_g1_metadata.num_joints, 0.25); + } + + // Parse joint_names + if (metadata_map.count("joint_names")) { + g_g1_metadata.joint_names = parse_csv_strings(metadata_map["joint_names"]); + } + + } catch (const std::exception& e) { + // If metadata parsing fails, use defaults + g_g1_metadata.default_joint_pos.assign(29, 0.0); + g_g1_metadata.action_scale.assign(29, 0.25); + g_g1_metadata.num_joints = 29; + } + + // Arm joint indices (15-28) - hard-coded as in simulate_g1.py + g_g1_metadata.arm_joint_indices = {15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28}; + + // Calculate observation dimension + g_g1_metadata.obs_dim = 3 + 3 + 3 + g_g1_metadata.num_joints * 3 + 3; + + g_g1_metadata_loaded = true; +} + +static std::string get_g1_policy_path() { + // Check G1_EXTENSIONS_POLICY_DIR env var first (set by g1_extensions/__init__.py) + const char* policy_dir = std::getenv("G1_EXTENSIONS_POLICY_DIR"); + if (policy_dir) { + return std::string(policy_dir) + "/g1_velocity_policy.onnx"; + } + // Fallback to relative path + return std::string("g1_extensions/policy/g1_velocity_policy.onnx"); +} + +// ============================================================================= +// G1ThreadPool Implementation +// ============================================================================= + +G1ThreadPool::G1ThreadPool(int num_threads) + : num_threads_(num_threads), stop_(false), active_workers_(0), total_tasks_(0), completed_tasks_(0) { + if (num_threads_ > 0) { + threads_.reserve(num_threads_); + for (int i = 0; i < num_threads_; ++i) { + threads_.emplace_back(&G1ThreadPool::worker_thread, this); + } + } +} + +G1ThreadPool::~G1ThreadPool() { + if (num_threads_ > 0) { + { + std::unique_lock lock(queue_mutex_); + stop_ = true; + } + condition_.notify_all(); + for (std::thread &worker : threads_) { + worker.join(); + } + } +} + +void G1ThreadPool::execute_parallel(std::function func, int total_work) { + if (num_threads_ == 0) { + // Single-threaded execution + for (int i = 0; i < total_work; ++i) { + func(i); + } + } else { + // Multi-threaded execution + { + std::unique_lock lock(queue_mutex_); + total_tasks_ = total_work; + completed_tasks_ = 0; + + for (int i = 0; i < total_work; ++i) { + tasks_.push([func, i]() { func(i); }); + } + } + condition_.notify_all(); + + std::unique_lock lock(queue_mutex_); + finished_.wait(lock, [this]() { return completed_tasks_ == total_tasks_; }); + } +} + +void G1ThreadPool::worker_thread() { + for (;;) { + std::function task; + { + std::unique_lock lock(queue_mutex_); + condition_.wait(lock, [this]() { return stop_ || !tasks_.empty(); }); + + if (stop_ && tasks_.empty()) + return; + + task = std::move(tasks_.front()); + tasks_.pop(); + } + + task(); + + { + std::unique_lock lock(queue_mutex_); + completed_tasks_++; + if (completed_tasks_ == total_tasks_) { + finished_.notify_one(); + } + } + } +} + +// ============================================================================= +// G1Rollout Implementation +// ============================================================================= + +G1Rollout::G1Rollout(int nthread, double cutoff_time) : num_threads_(nthread), cutoff_time_(cutoff_time) { + initialize_policy(); + if (num_threads_ != 0) { + thread_pool_ = std::make_unique(num_threads_); + } +} + +G1Rollout::~G1Rollout() { + close(); +} + +void G1Rollout::close() { + if (!closed_) { + thread_pool_.reset(); + policy_.reset(); + onnx_session_.reset(); + closed_ = true; + } +} + +void G1Rollout::initialize_policy() { + std::string policy_path = get_g1_policy_path(); + onnx_session_ = allocate_shared_session(policy_path); + parse_g1_metadata(onnx_session_); + policy_ = std::make_unique(onnx_session_); +} + +py::tuple G1Rollout::rollout( + const std::vector& models, + const std::vector& data, + const py::array_t& initial_state, + const py::array_t& controls +) { + if (closed_) { + throw std::runtime_error("Rollout requested after object was closed"); + } + + int B = (int)models.size(); + if (B == 0 || B != (int)data.size()) { + throw std::runtime_error("models/data must have same non-zero length"); + } + + int horizon = (int)controls.shape(1); + const mjModel* m0 = models[0]; + int nq = m0->nq; + int nv = m0->nv; + int nu = m0->nu; + int nsens = m0->nsensordata; + int nstate = nq + nv; + + if (initial_state.ndim() != 2 || initial_state.shape(0) != B || initial_state.shape(1) != nstate) { + throw std::runtime_error("initial_state must be a 2D array of shape (B, nq+nv)"); + } + + // Controls should be (B, horizon, 17) for G1 - [vx, vy, wz, left_arm(7), right_arm(7)] + // If controls are all zeros for arm indices, policy output will be used instead + if (controls.ndim() != 3 || controls.shape(0) != B || controls.shape(2) != 17) { + throw std::runtime_error("controls must be a 3D array of shape (B, horizon, 17)"); + } + + std::vector states_buf(B * (horizon + 1) * nstate); + std::vector sens_buf(B * horizon * nsens); + + auto controls_unchecked = controls.unchecked<3>(); + const double* x0_ptr = initial_state.data(); + + std::vector> prev_policy(B, std::vector(g_g1_metadata.num_joints, 0.0f)); + + { + py::gil_scoped_release release; + + auto execute_work = [&](int i) { + auto start_time = std::chrono::high_resolution_clock::now(); + + mjData* d = data[i]; + const mjModel* m = models[i]; + + d->time = 0.0; + const double* x0_i = x0_ptr + i * nstate; + mj_setState(m, d, x0_i, mjSTATE_QPOS | mjSTATE_QVEL); + mj_forward(m, d); + mju_zero(d->qacc_warmstart, m->nv); + + double* st_ptr = &states_buf[i * (horizon + 1) * nstate]; + double* se_ptr = &sens_buf[i * horizon * nsens]; + + // Store initial state + for (int j = 0; j < nq; j++) st_ptr[j] = d->qpos[j]; + for (int j = 0; j < nv; j++) st_ptr[nq + j] = d->qvel[j]; + + int base_qpos_start, base_qvel_start, leg_qpos_start, leg_qvel_start; + compute_indices(m, base_qpos_start, base_qvel_start, leg_qpos_start, leg_qvel_start); + + for (int t = 0; t < horizon; t++) { + // Check timeout before each step + auto current_time = std::chrono::high_resolution_clock::now(); + auto elapsed = std::chrono::duration(current_time - start_time).count(); + if (elapsed > cutoff_time_) { + // Timeout reached, fill remaining states with current state and return + for (int remaining_t = t; remaining_t < horizon; remaining_t++) { + for (int j = 0; j < nq; j++) st_ptr[(remaining_t + 1) * nstate + j] = d->qpos[j]; + for (int j = 0; j < nv; j++) st_ptr[(remaining_t + 1) * nstate + nq + j] = d->qvel[j]; + for (int j = 0; j < nsens; j++) se_ptr[remaining_t * nsens + j] = d->sensordata[j]; + } + return; + } + + // Get full control command [vx, vy, wz, left_arm(7), right_arm(7)] + std::vector obs; + double cmd_vel_buf[3]; + double arm_cmd_buf[14]; // left_arm(7) + right_arm(7) + + // Extract velocity commands + for (int j = 0; j < 3; j++) { + cmd_vel_buf[j] = static_cast(controls_unchecked(i, t, j)); + } + + // Extract arm commands + for (int j = 0; j < 14; j++) { + arm_cmd_buf[j] = static_cast(controls_unchecked(i, t, 3 + j)); + } + + // Build observation for policy (using velocity commands only) + build_observation(m, d, cmd_vel_buf, prev_policy[i], + base_qpos_start, base_qvel_start, leg_qpos_start, leg_qvel_start, obs); + + // Run policy + auto policy_out_vec = policy_->run(obs); + + // Compute control from policy output and user arm commands + std::vector ctrl; + compute_control_from_policy(policy_out_vec.data(), arm_cmd_buf, ctrl); + + if ((int)ctrl.size() != nu) { + throw std::runtime_error("Computed control size does not match model nu"); + } + for (int j = 0; j < nu; j++) d->ctrl[j] = ctrl[j]; + + // Step simulation + mj_step(m, d); + prev_policy[i] = std::move(policy_out_vec); + + // Store state and sensor data + for (int j = 0; j < nq; j++) st_ptr[(t + 1) * nstate + j] = d->qpos[j]; + for (int j = 0; j < nv; j++) st_ptr[(t + 1) * nstate + nq + j] = d->qvel[j]; + for (int j = 0; j < nsens; j++) se_ptr[t * nsens + j] = d->sensordata[j]; + } + }; + + if (num_threads_ == 0) { + // Single-threaded execution + for (int i = 0; i < B; ++i) { + execute_work(i); + } + } else { + // Multi-threaded execution + thread_pool_->execute_parallel(execute_work, B); + } + } + + auto states_arr = make_array_owned_g1(states_buf, B, horizon + 1, nstate); + auto sens_arr = make_array_owned_g1(sens_buf, B, horizon, nsens); + return py::make_tuple(states_arr, sens_arr); +} + +G1Rollout* G1Rollout::__enter__() { + return this; +} + +void G1Rollout::__exit__(py::object exc_type, py::object exc_val, py::object exc_tb) { + close(); +} + +int G1Rollout::get_num_threads() const { + return num_threads_; +} + +// Helper method implementations +void G1Rollout::compute_indices(const mjModel* m, int& base_qpos_start, int& base_qvel_start, + int& leg_qpos_start, int& leg_qvel_start) { + // Find the free joint (root body) + int free_joint_idx = -1; + for (int j = 0; j < m->njnt; j++) { + if (m->jnt_type[j] == mjJNT_FREE) { + free_joint_idx = j; + break; + } + } + if (free_joint_idx == -1) { + free_joint_idx = 0; + } + base_qpos_start = m->jnt_qposadr[free_joint_idx]; + base_qvel_start = m->jnt_dofadr[free_joint_idx]; + // G1 has 29 controlled joints starting after the free joint + leg_qpos_start = base_qpos_start + 7; // After pos(3) + quat(4) + leg_qvel_start = base_qvel_start + 6; // After lin_vel(3) + ang_vel(3) +} + +void G1Rollout::build_observation(const mjModel* m, mjData* d, const double* command_ptr, + const std::vector& prev_policy, + int base_qpos_start, int base_qvel_start, + int leg_qpos_start, int leg_qvel_start, + std::vector& obs_out) { + obs_out.resize(g_g1_metadata.obs_dim); + int off = 0; + + // Get root body quaternion + double quat[4]; + for (int i = 0; i < 4; i++) { + quat[i] = d->qpos[base_qpos_start + 3 + i]; + } + + // Compute inverse quaternion for body frame transformations + double invq[4]; + mju_negQuat(invq, quat); + + // Compute rotation matrix from quaternion + double rot_mat[9]; + mju_quat2Mat(rot_mat, quat); + + // Base linear velocity in body frame (3) + double base_lin_vel_world[3]; + for (int i = 0; i < 3; i++) { + base_lin_vel_world[i] = d->qvel[base_qvel_start + i]; + } + double base_lin_vel_body[3]; + mju_rotVecQuat(base_lin_vel_body, base_lin_vel_world, invq); + for (int i = 0; i < 3; i++) obs_out[off++] = static_cast(base_lin_vel_body[i]); + + // Base angular velocity in body frame (3) + double base_ang_vel_world[3]; + for (int i = 0; i < 3; i++) { + base_ang_vel_world[i] = d->qvel[base_qvel_start + 3 + i]; + } + double base_ang_vel_body[3]; + mju_rotVecQuat(base_ang_vel_body, base_ang_vel_world, invq); + for (int i = 0; i < 3; i++) obs_out[off++] = static_cast(base_ang_vel_body[i]); + + // Projected gravity in body frame (3) + double gvec[3] = {0.0, 0.0, -1.0}; + double gvec_rotated[3]; + mju_rotVecQuat(gvec_rotated, gvec, invq); + for (int i = 0; i < 3; i++) obs_out[off++] = static_cast(gvec_rotated[i]); + + // Joint positions relative to default + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + double joint_pos = d->qpos[leg_qpos_start + i] - g_g1_metadata.default_joint_pos[i]; + obs_out[off++] = static_cast(joint_pos); + } + + // Joint velocities + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + obs_out[off++] = static_cast(d->qvel[leg_qvel_start + i]); + } + + // Previous actions + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + obs_out[off++] = (i < (int)prev_policy.size() ? prev_policy[i] : 0.0f); + } + + // Command (3): [vx, vy, wz] + for (int i = 0; i < 3; i++) { + obs_out[off++] = static_cast(command_ptr[i]); + } +} + +void G1Rollout::compute_control_from_policy(const float* policy_out, + const double* arm_commands, + std::vector& ctrl_out) { + ctrl_out.resize(g_g1_metadata.num_joints); + + // Apply action scaling and add default positions for all joints + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + ctrl_out[i] = static_cast(policy_out[i]) * g_g1_metadata.action_scale[i] + g_g1_metadata.default_joint_pos[i]; + } + + // Override arm joints with user commands if provided (non-zero) + // arm_commands layout: [left_arm(7), right_arm(7)] + // arm_joint_indices contains the indices of arm joints in the full joint array + for (size_t i = 0; i < g_g1_metadata.arm_joint_indices.size(); i++) { + int joint_idx = g_g1_metadata.arm_joint_indices[i]; + double user_cmd = arm_commands[i]; + + // If user command is non-zero, use it; otherwise use policy output + if (std::abs(user_cmd) > 1e-8) { + ctrl_out[joint_idx] = user_cmd; + } + // else: keep policy output (already set above) + } +} + +// ============================================================================= +// SimG1 - Single-step simulation with G1 policy +// ============================================================================= + +py::array_t SimG1( + const mjModel* model, + mjData* data, + const py::array_t& x0, + const py::array_t& command, + const py::array_t& prev_policy +) { + static std::shared_ptr onnx_session = nullptr; + static std::unique_ptr policy = nullptr; + + // Initialize policy on first call + if (!onnx_session || !policy) { + std::string policy_path = get_g1_policy_path(); + onnx_session = allocate_shared_session(policy_path); + parse_g1_metadata(onnx_session); + policy = std::make_unique(onnx_session); + } + + int nq = model->nq; + int nv = model->nv; + int nu = model->nu; + + if (x0.size() != nq + nv) { + throw std::runtime_error("x0 size must equal nq + nv"); + } + if (command.size() != 17) { + throw std::runtime_error("command size must be 17 [vx, vy, wz, left_arm(7), right_arm(7)]"); + } + if (prev_policy.size() != g_g1_metadata.num_joints) { + throw std::runtime_error("prev_policy size must match num_joints from metadata"); + } + + // Set initial state + const double* x0_ptr = x0.data(); + mj_setState(model, data, x0_ptr, mjSTATE_QPOS | mjSTATE_QVEL); + mj_forward(model, data); + + // Convert prev_policy to vector + std::vector prev_policy_vec(g_g1_metadata.num_joints); + const float* prev_policy_ptr = prev_policy.data(); + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + prev_policy_vec[i] = prev_policy_ptr[i]; + } + + // Get command data - extract velocity commands and arm commands + const double* cmd_ptr = command.data(); + double cmd_vel[3] = {cmd_ptr[0], cmd_ptr[1], cmd_ptr[2]}; + double arm_cmd[14]; // left_arm(7) + right_arm(7) + for (int i = 0; i < 14; i++) { + arm_cmd[i] = cmd_ptr[3 + i]; + } + + // Compute indices for root and joint positions/velocities + int base_qpos_start, base_qvel_start, leg_qpos_start, leg_qvel_start; + int free_joint_idx = -1; + for (int j = 0; j < model->njnt; j++) { + if (model->jnt_type[j] == mjJNT_FREE) { + free_joint_idx = j; + break; + } + } + if (free_joint_idx == -1) { + free_joint_idx = 0; + } + base_qpos_start = model->jnt_qposadr[free_joint_idx]; + base_qvel_start = model->jnt_dofadr[free_joint_idx]; + leg_qpos_start = base_qpos_start + 7; + leg_qvel_start = base_qvel_start + 6; + + // Build observation vector + std::vector obs(g_g1_metadata.obs_dim); + int off = 0; + + // Get root body quaternion + double quat[4]; + for (int i = 0; i < 4; i++) { + quat[i] = data->qpos[base_qpos_start + 3 + i]; + } + + // Compute inverse quaternion for body frame transformations + double invq[4]; + mju_negQuat(invq, quat); + + // Base linear velocity in body frame (3) + double base_lin_vel_world[3]; + for (int i = 0; i < 3; i++) { + base_lin_vel_world[i] = data->qvel[base_qvel_start + i]; + } + double base_lin_vel_body[3]; + mju_rotVecQuat(base_lin_vel_body, base_lin_vel_world, invq); + for (int i = 0; i < 3; i++) obs[off++] = static_cast(base_lin_vel_body[i]); + + // Base angular velocity in body frame (3) + double base_ang_vel_world[3]; + for (int i = 0; i < 3; i++) { + base_ang_vel_world[i] = data->qvel[base_qvel_start + 3 + i]; + } + double base_ang_vel_body[3]; + mju_rotVecQuat(base_ang_vel_body, base_ang_vel_world, invq); + for (int i = 0; i < 3; i++) obs[off++] = static_cast(base_ang_vel_body[i]); + + // Projected gravity in body frame (3) + double gvec[3] = {0.0, 0.0, -1.0}; + double gvec_rotated[3]; + mju_rotVecQuat(gvec_rotated, gvec, invq); + for (int i = 0; i < 3; i++) obs[off++] = static_cast(gvec_rotated[i]); + + // Joint positions relative to default + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + double joint_pos = data->qpos[leg_qpos_start + i] - g_g1_metadata.default_joint_pos[i]; + obs[off++] = static_cast(joint_pos); + } + + // Joint velocities + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + obs[off++] = static_cast(data->qvel[leg_qvel_start + i]); + } + + // Previous actions + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + obs[off++] = prev_policy_vec[i]; + } + + // Command (3): [vx, vy, wz] - use velocity commands only + for (int i = 0; i < 3; i++) { + obs[off++] = static_cast(cmd_vel[i]); + } + + // Run policy + auto policy_out_vec = policy->run(obs); + + // Compute control from policy output + std::vector ctrl(g_g1_metadata.num_joints); + + // Apply action scaling and add default positions for all joints + for (int i = 0; i < g_g1_metadata.num_joints; i++) { + ctrl[i] = static_cast(policy_out_vec[i]) * g_g1_metadata.action_scale[i] + g_g1_metadata.default_joint_pos[i]; + } + + // Override arm joints with user commands if provided (non-zero) + // arm_cmd layout: [left_arm(7), right_arm(7)] + for (size_t i = 0; i < g_g1_metadata.arm_joint_indices.size(); i++) { + int joint_idx = g_g1_metadata.arm_joint_indices[i]; + double user_cmd = arm_cmd[i]; + + // If user command is non-zero, use it; otherwise use policy output + if (std::abs(user_cmd) > 1e-8) { + ctrl[joint_idx] = user_cmd; + } + // else: keep policy output (already set above) + } + + // Apply control and step simulation + if (nu != g_g1_metadata.num_joints) { + throw std::runtime_error("Model nu does not match num_joints from metadata"); + } + for (int j = 0; j < nu; j++) { + data->ctrl[j] = ctrl[j]; + } + mj_step(model, data); + + // Return new policy output as numpy array + std::vector shape = {g_g1_metadata.num_joints}; + std::vector strides = {sizeof(float)}; + auto heap_buf = new std::vector(std::move(policy_out_vec)); + py::capsule free_when_done(heap_buf, [](void *p) { delete reinterpret_cast*>(p); }); + return py::array_t(shape, strides, heap_buf->data(), free_when_done); +} diff --git a/g1_extensions/g1_rollout.h b/g1_extensions/g1_rollout.h new file mode 100644 index 0000000..8586091 --- /dev/null +++ b/g1_extensions/g1_rollout.h @@ -0,0 +1,130 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; + +// Forward declaration +class OnnxPolicy; + +/** + * Simple thread pool for parallel execution + */ +class G1ThreadPool { +public: + explicit G1ThreadPool(int num_threads); + ~G1ThreadPool(); + + // Execute function across all threads + void execute_parallel(std::function func, int total_work); + + // Get thread count + int get_num_threads() const { return num_threads_; } + +private: + int num_threads_; + std::vector threads_; + std::queue> tasks_; + std::mutex queue_mutex_; + std::condition_variable condition_; + std::condition_variable finished_; + std::atomic stop_; + std::atomic active_workers_; + std::atomic total_tasks_; + std::atomic completed_tasks_; + + void worker_thread(); +}; + +/** + * G1 Rollout class mimicking mujoco.rollout.Rollout API + */ +class G1Rollout { +public: + /** + * Constructor + * @param nthread Number of threads (0 = single-threaded, >0 = multi-threaded) + * @param cutoff_time Maximum time allowed per rollout in seconds (default 0.2s) + */ + explicit G1Rollout(int nthread = 0, double cutoff_time = 0.2); + + /** + * Destructor - cleans up resources + */ + ~G1Rollout(); + + /** + * Close the rollout object and cleanup resources + */ + void close(); + + /** + * Perform rollout with G1-specific ONNX policy + * Matches mujoco.rollout.Rollout.rollout() API signature + */ + py::tuple rollout( + const std::vector& models, + const std::vector& data, + const py::array_t& initial_state, + const py::array_t& controls + ); + + /** + * Context manager support + */ + G1Rollout* __enter__(); + void __exit__(py::object exc_type, py::object exc_val, py::object exc_tb); + + /** + * Get the number of threads + */ + int get_num_threads() const; + +private: + int num_threads_; + double cutoff_time_; + std::unique_ptr thread_pool_; + std::shared_ptr onnx_session_; + std::unique_ptr policy_; + bool closed_ = false; + + // Helper methods for rollout computation + void compute_indices(const mjModel* m, int& base_qpos_start, int& base_qvel_start, + int& leg_qpos_start, int& leg_qvel_start); + + void build_observation(const mjModel* m, mjData* d, const double* command_ptr, + const std::vector& prev_policy, + int base_qpos_start, int base_qvel_start, + int leg_qpos_start, int leg_qvel_start, + std::vector& obs_out); + + void compute_control_from_policy(const float* policy_out, + const double* arm_commands, + std::vector& ctrl_out); + + // Initialize ONNX session and policy + void initialize_policy(); +}; + +// Utility function to create numpy arrays from C++ vectors +py::array_t make_array_owned_g1(std::vector& buf, int B, int T, int D); + +// Single-step simulation function for G1 +py::array_t SimG1( + const mjModel* model, + mjData* data, + const py::array_t& x0, + const py::array_t& command, + const py::array_t& prev_policy +); diff --git a/g1_extensions/policy/g1_velocity_policy.onnx b/g1_extensions/policy/g1_velocity_policy.onnx new file mode 100644 index 0000000..a2394c6 Binary files /dev/null and b/g1_extensions/policy/g1_velocity_policy.onnx differ diff --git a/g1_extensions/pyproject.toml b/g1_extensions/pyproject.toml new file mode 100644 index 0000000..b5fbb4b --- /dev/null +++ b/g1_extensions/pyproject.toml @@ -0,0 +1,20 @@ +[build-system] +requires = ["scikit-build-core[pyproject]", "pybind11", "mujoco"] +build-backend = "scikit_build_core.build" + +[project] +name = "g1-extensions" +version = "0.0.1" + +[project.optional-dependencies] +dev = [ + "ninja", + "pybind11", +] + +[tool.scikit-build] +cmake.source-dir = "." +wheel.packages = ["g1_extensions"] +cmake.build-type = "Release" +build-dir = "build" +cmake.args = ["-DEigen3_DIR=$ENV{CONDA_PREFIX}/lib/cmake/eigen3"] diff --git a/g1_extensions/rollout.cpp b/g1_extensions/rollout.cpp new file mode 100644 index 0000000..059ab09 --- /dev/null +++ b/g1_extensions/rollout.cpp @@ -0,0 +1,271 @@ +#include "rollout.h" +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; + +// ========== Thread Pool Implementation ========== + +PersistentThreadPool::PersistentThreadPool(int num_threads) + : num_threads_(num_threads), stop_(false), active_workers_(0), total_tasks_(0), completed_tasks_(0) { + threads_.reserve(num_threads); + for (int i = 0; i < num_threads; ++i) { + threads_.emplace_back(&PersistentThreadPool::worker_thread, this); + } +} + +PersistentThreadPool::~PersistentThreadPool() { + { + std::unique_lock lock(queue_mutex_); + stop_ = true; + } + condition_.notify_all(); + for (std::thread& thread : threads_) { + if (thread.joinable()) { + thread.join(); + } + } +} + +void PersistentThreadPool::worker_thread() { + while (true) { + std::function task; + { + std::unique_lock lock(queue_mutex_); + condition_.wait(lock, [this] { return stop_ || !tasks_.empty(); }); + + if (stop_ && tasks_.empty()) { + return; + } + + if (!tasks_.empty()) { + task = std::move(tasks_.front()); + tasks_.pop(); + active_workers_++; + } + } + + if (task) { + task(); + + std::unique_lock lock(queue_mutex_); + active_workers_--; + completed_tasks_++; + if (completed_tasks_ == total_tasks_) { + finished_.notify_one(); + } + } + } +} + +void PersistentThreadPool::execute_parallel(std::function func, int total_work) { + { + std::unique_lock lock(queue_mutex_); + total_tasks_ = total_work; + completed_tasks_ = 0; + + for (int i = 0; i < total_work; ++i) { + tasks_.emplace([func, i] { func(i); }); + } + } + + condition_.notify_all(); + + // Wait for all tasks to complete + std::unique_lock lock(queue_mutex_); + finished_.wait(lock, [this] { return completed_tasks_ == total_tasks_; }); +} + +// Thread Pool Manager Implementation +ThreadPoolManager& ThreadPoolManager::instance() { + static ThreadPoolManager instance; + return instance; +} + +PersistentThreadPool* ThreadPoolManager::get_pool(int num_threads) { + std::lock_guard lock(pool_mutex_); + + if (!current_pool_ || current_num_threads_ != num_threads) { + current_pool_.reset(); // Destroy old pool + current_pool_ = std::make_unique(num_threads); + current_num_threads_ = num_threads; + } + + return current_pool_.get(); +} + +void ThreadPoolManager::shutdown() { + std::lock_guard lock(pool_mutex_); + current_pool_.reset(); + current_num_threads_ = 0; +} + +// ========== End Thread Pool Implementation ========== + +py::array_t make_array(std::vector& buf, + int B, int T, int D) { + std::vector shape = { B, T, D }; + std::vector strides = { + static_cast(sizeof(double) * T * D), + static_cast(sizeof(double) * D), + static_cast(sizeof(double)) + }; + + // Move buf onto the heap so we can own it in the capsule: + auto heap_buf = new std::vector(std::move(buf)); + // Create a capsule that will delete the vector when the array is gone: + py::capsule free_when_done(heap_buf, [](void *p) { + delete reinterpret_cast*>(p); + }); + + // Build the array pointing into heap_buf->data() and owning the capsule: + return py::array_t( + shape, strides, + heap_buf->data(), + free_when_done + ); +} + + +py::tuple Rollout( + const std::vector& models, + const std::vector& data, + const py::array_t& x0, + const py::array_t& controls +) { + int B = (int)models.size(); + if (B == 0 || B != (int)data.size()) { + throw std::runtime_error("models/data must have same non-zero length"); + } + + int horizon = (int)controls.shape(1); // controls: 3D of shape (B, horizon, nu) + + // dims from first model + const mjModel* m0 = models[0]; + int nq = m0->nq; + int nv = m0->nv; + int nu = m0->nu; + int nsens = m0->nsensordata; + int nstate = nq + nv; + + // x0: 2D of shape (B, nstate) for batched initial states + if (x0.ndim() != 2 || x0.shape(0) != B || x0.shape(1) != nstate) { + throw std::runtime_error("x0 must be a 2D array of shape (B, nq+nv)"); + } + const double* x0_ptr = x0.data(); + + // allocate outputs + std::vector states_buf(B * (horizon + 1) * nstate); // +1 for initial state + std::vector sens_buf(B * horizon * nsens); + + { + py::gil_scoped_release release; + + auto controls_unchecked = controls.unchecked<3>(); + + #pragma omp parallel for + for (int i = 0; i < B; i++) { + try { + mjData* d = data[i]; + + // set initial qpos+qvel for this batch + d->time = 0.0; + const double* x0_i = x0_ptr + i * nstate; + mj_setState(models[i], d, x0_i, mjSTATE_QPOS | mjSTATE_QVEL); + mj_forward(models[i], d); + mju_zero(d->qacc_warmstart, m0->nv); + + double* st_ptr = &states_buf[i * (horizon + 1) * nstate]; + double* se_ptr = &sens_buf[i * horizon * nsens]; + + // Store initial state + for (int j = 0; j < nq; j++) st_ptr[j] = d->qpos[j]; + for (int j = 0; j < nv; j++) st_ptr[nq + j] = d->qvel[j]; + std::vector current_state(nstate); + for (int j = 0; j < nq; j++) current_state[j] = d->qpos[j]; + for (int j = 0; j < nv; j++) current_state[nq + j] = d->qvel[j]; + + for (int t = 0; t < horizon; t++) { + for (int j = 0; j < nu; j++) d->ctrl[j] = controls_unchecked(i, t, j); + + // Step simulation + mj_step(models[i], d); + + // Record new state + for (int j = 0; j < nq; j++) current_state[j] = d->qpos[j]; + for (int j = 0; j < nv; j++) current_state[nq + j] = d->qvel[j]; + + // Store in output buffers + for (int j = 0; j < nstate; j++) { + st_ptr[(t + 1) * nstate + j] = current_state[j]; + } + for (int j = 0; j < nsens; j++) { + se_ptr[t * nsens + j] = d->sensordata[j]; + } + } + + } catch (const std::exception& e) { + std::cerr << "Error in rollout thread " << i << ": " << e.what() << std::endl; + // Fill with zeros as fallback + double* st_ptr = &states_buf[i * (horizon + 1) * nstate]; + double* se_ptr = &sens_buf[i * horizon * nsens]; + + for (int t = 0; t <= horizon; t++) { + for (int j = 0; j < nstate; j++) { + st_ptr[t * nstate + j] = 0.0; + } + } + for (int t = 0; t < horizon; t++) { + for (int j = 0; j < nsens; j++) { + se_ptr[t * nsens + j] = 0.0; + } + } + } + } + } + + auto states_arr = make_array(states_buf, B, horizon + 1, nstate); + auto sens_arr = make_array(sens_buf, B, horizon, nsens); + + return py::make_tuple(states_arr, sens_arr); +} + +void Sim( + const mjModel* model, + mjData* data, + const py::array_t& x0, + const py::array_t& controls +) { + // dims from model + int nq = model->nq; + int nv = model->nv; + int nu = model->nu; + int nstate = nq + nv; + + // x0: 1D of shape (nstate) + if (x0.ndim() != 1 || x0.shape(0) != nstate) { + throw std::runtime_error("x0 must be a 1D array of shape (nq+nv)"); + } + if (controls.ndim() != 1 || controls.shape(0) != nu) { + throw std::runtime_error("controls must be a 1D array of shape (nu)"); + } + const double* x0_ptr = x0.data(); + const double* ctrl_ptr = controls.data(); + + // set initial qpos+qvel + data->time = 0.0; + mj_setState(model, data, x0_ptr, mjSTATE_QPOS | mjSTATE_QVEL); + mj_forward(model, data); + mju_zero(data->qacc_warmstart, nv); + + // set controls + for (int j = 0; j < nu; j++) data->ctrl[j] = ctrl_ptr[j]; + + // step simulation + mj_step(model, data); +} diff --git a/g1_extensions/rollout.h b/g1_extensions/rollout.h new file mode 100644 index 0000000..2944113 --- /dev/null +++ b/g1_extensions/rollout.h @@ -0,0 +1,73 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; + +// Utility function to create numpy arrays from C++ vectors +py::array_t make_array(std::vector& buf, int B, int T, int D); + +// Thread pool for persistent thread management +class PersistentThreadPool { +public: + PersistentThreadPool(int num_threads); + ~PersistentThreadPool(); + + // Execute a function across the thread pool + void execute_parallel(std::function func, int total_work); + + int get_num_threads() const { return num_threads_; } + +private: + int num_threads_; + std::vector threads_; + std::queue> tasks_; + std::mutex queue_mutex_; + std::condition_variable condition_; + std::condition_variable finished_; + bool stop_; + int active_workers_; + int total_tasks_; + int completed_tasks_; + + void worker_thread(); +}; + +// Global persistent thread pool manager +class ThreadPoolManager { +public: + static ThreadPoolManager& instance(); + PersistentThreadPool* get_pool(int num_threads); + void shutdown(); + +private: + std::mutex pool_mutex_; + std::unique_ptr current_pool_; + int current_num_threads_ = 0; +}; + +py::tuple Rollout( + const std::vector& models, + const std::vector& data, + const py::array_t& x0, + const py::array_t& controls +); + +void Sim( + const mjModel* model, + mjData* data, + const pybind11::array_t& x0, + const pybind11::array_t& controls +); diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000..4341469 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,6502 @@ +version: 6 +environments: + default: + channels: + - url: 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-Dpybind11_DIR=$(python -m pybind11 --cmakedir) +' +""" + +[tool.pixi.feature.dev.tasks.build-judo-ext] +depends-on = ["configure-judo-ext"] +cmd = """ +sh -c ' +ninja -C .judo-src/build && +SITE=$(python -c "import site; print(site.getsitepackages()[0])") && +SO=$(ls .judo-src/mujoco_extensions/policy_rollout/policy_rollout_pybind*.so 2>/dev/null | head -1) && +if [ -z "$SO" ]; then echo "ERROR: .so not found in .judo-src/mujoco_extensions/policy_rollout/"; exit 1; fi && +if [ "$(uname)" = "Darwin" ]; then + install_name_tool -add_rpath @loader_path/../../mujoco "$SO" 2>/dev/null || true +fi && +cp "$SO" "$SITE/mujoco_extensions/policy_rollout/" +' +""" + +[tool.pixi.tasks] + +[tool.pixi.tasks.configure] +cmd = """ +sh -c 'mkdir -p g1_extensions/build && \ +cmake -S g1_extensions -B g1_extensions/build \ + -G Ninja \ + -DPython_ROOT_DIR=$CONDA_PREFIX \ + -DPython_EXECUTABLE=$CONDA_PREFIX/bin/python \ + -Dpybind11_DIR=$(python -m pybind11 --cmakedir)' +""" + +[tool.pixi.tasks.build-g1-ext] +depends-on = ["configure"] +cwd = "g1_extensions/build" +cmd = "ninja _g1_extensions" + +[tool.pixi.tasks.build] +depends-on = ["build-g1-ext", "build-judo-ext"] + +[tool.pixi.tasks.clean] +cwd = "g1_extensions" +cmd = "rm -rf build/" + +[tool.pytest.ini_options] +markers = [ + "g1_extensions: marks tests that require the optional g1_extensions extension", +] + +[tool.ruff] +line-length = 120 +extend-exclude = ["g1_extensions", ".judo-src"] + +[tool.ruff.lint] +pydocstyle.convention = "google" +select = [ + "ANN", # annotations + "N", # naming conventions + "D", # docstrings + "B", # flake8 bugbear + "E", # pycodestyle errors + "F", # Pyflakes rules + "I", # isort formatting + "PLC", # Pylint convention warnings + "PLE", # Pylint errors + "PLR", # Pylint refactor recommendations + "PLW", # Pylint warnings +] +ignore = [ + "ANN001", # Missing type annotation for function argument + "ANN002", # Missing type annotation for *args + "ANN003", # Missing type annotation for **kwargs + "ANN201", # Missing return type annotation for public function + "ANN202", # Missing return type annotation for private function + "ANN204", # Missing return type annotation for special method + "ANN206", # Missing return type annotation for classmethod + "ANN401", # Dynamically typed expressions (typing.Any) are disallowed + "D100", # missing docstring in public module + "D101", # missing docstring in public class + "D102", # missing docstring in public method + "D103", # missing docstring in public function + "D104", # missing docstring in public package + "D105", # missing docstring in magic method + "D107", # missing docstring in __init__ + "D203", # blank line before class docstring + "D211", # no blank line before class + "D212", # multi-line docstring summary at first line + "D213", # multi-line docstring summary at second line + "E741", # Ambiguous variable name + "E501", # Line too long + "E721", # Do not compare types, use isinstance() + "F722", # Forward annotation false positive from jaxtyping + "F821", # Forward annotation false positive from jaxtyping + "N803", # Argument name should be lowercase + "N806", # Variable in function should be lowercase + "N812", # lowercase imported as non-lowercase + "N817", # CamelCase imported as acronym + "PLR0911", # Too many return statements + "PLR0912", # Too many branches + "PLR0913", # Too many arguments in function definition + "PLR0915", # Too many statements in function + "PLR2004", # magic numbers + "PLC0415", # import not at top-level (intentional lazy/conditional imports) + "E402", # module-level import not at top of file (side-effect imports must run first) + "E731", # lambda assignment (used for closures in _make_condition_checker) + "D415", # first line of docstring should end with period + "N801", # class name should use CapWords (POLICY_MODE is an intentional constant-style enum) + "PLW0108", # unnecessary lambda (closures capturing variables are intentional) + "F841", # local variable assigned but never used (sensor readouts kept for clarity) +] + +[tool.pyright] +include = ["sumo", "tests"] +exclude = [ + "**/__pycache__", +] +stubPath = "typings" +reportAttributeAccessIssue = "warning" diff --git a/sumo/__init__.py b/sumo/__init__.py new file mode 100644 index 0000000..ecf03f0 --- /dev/null +++ b/sumo/__init__.py @@ -0,0 +1,40 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +import os +import sys +from pathlib import Path + +PACKAGE_ROOT = Path(__file__).resolve().parent +MODEL_PATH = PACKAGE_ROOT / "models" + + +def _prepend_env_path(env_var: str, path: Path) -> None: + """Prepend a path-like environment variable without duplicating entries.""" + path_str = str(path) + current = os.environ.get(env_var, "") + entries = [entry for entry in current.split(os.pathsep) if entry] + if path_str in entries: + return + os.environ[env_var] = os.pathsep.join([path_str, *entries]) if entries else path_str + + +def _prefer_active_env_libs() -> None: + """Prefer shared libraries from the active Pixi/Conda environment. + + Some dependencies import `pyarrow` before `numpy`, which can otherwise bind + against an older system `libstdc++` and break later NumPy imports. + """ + conda_prefix = os.environ.get("CONDA_PREFIX") + if not conda_prefix: + return + + lib_dir = Path(conda_prefix) / "lib" + if not lib_dir.is_dir(): + return + + _prepend_env_path("LD_LIBRARY_PATH", lib_dir) + if sys.platform == "darwin": + _prepend_env_path("DYLD_FALLBACK_LIBRARY_PATH", lib_dir) + + +_prefer_active_env_libs() diff --git a/sumo/app/__init__.py b/sumo/app/__init__.py new file mode 100644 index 0000000..536dbd9 --- /dev/null +++ b/sumo/app/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. diff --git a/sumo/app/dora/__init__.py b/sumo/app/dora/__init__.py new file mode 100644 index 0000000..536dbd9 --- /dev/null +++ b/sumo/app/dora/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. diff --git a/sumo/app/dora/controller.py b/sumo/app/dora/controller.py new file mode 100644 index 0000000..982020e --- /dev/null +++ b/sumo/app/dora/controller.py @@ -0,0 +1,21 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.app.dora.controller import ControllerNode as JudoControllerNode + +import sumo.controller # noqa: F401 -- register controller/optimizer overrides +import sumo.tasks # noqa: F401 -- register all sumo tasks +from sumo.controller import make_controller + + +class ControllerNode(JudoControllerNode): + """Controller node that uses sumo's make_controller for G1 backend support.""" + + def __init__(self, init_task: str = "spot_box_push", **kwargs) -> None: + super().__init__( + init_task=init_task, + make_controller_fn=make_controller, + **kwargs, + ) + + +__all__ = ["ControllerNode"] diff --git a/sumo/app/dora/g1_simulation.py b/sumo/app/dora/g1_simulation.py new file mode 100644 index 0000000..958ef1b --- /dev/null +++ b/sumo/app/dora/g1_simulation.py @@ -0,0 +1,52 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""G1 simulation backend using sumo's local C++ policy wrapper.""" + +from typing import Callable + +import numpy as np +from judo.simulation.base import Simulation +from mujoco import MjData, MjModel + +from sumo.utils.extensions import require_g1_extensions + + +class SimBackendG1: + """Single-step simulation backend for G1 using the C++ policy wrapper.""" + + def __init__(self, task_to_sim_ctrl: Callable) -> None: + self.task_to_sim_ctrl = task_to_sim_ctrl + self.previous_policy_output = np.zeros(29) + + def sim(self, sim_model: MjModel, sim_data: MjData, sim_controls: np.ndarray) -> None: + """Conduct a simulation step.""" + g1_extensions = require_g1_extensions() + x0 = np.concatenate([sim_data.qpos, sim_data.qvel]) + controls = self.task_to_sim_ctrl(sim_controls).flatten() + self.previous_policy_output = g1_extensions.sim_g1( + sim_model, + sim_data, + x0, + controls, + self.previous_policy_output, + ) + + +class G1Simulation(Simulation): + """Simulation backend for G1 tasks using the local C++ policy wrapper.""" + + def __init__(self, **kwargs) -> None: + self._sim_backend: SimBackendG1 | None = None + super().__init__(**kwargs) + + def set_task(self, task_name: str) -> None: + super().set_task(task_name) + self._sim_backend = SimBackendG1(task_to_sim_ctrl=self.task.task_to_sim_ctrl) + + def step(self, command: np.ndarray) -> None: + if self.paused: + return + assert self._sim_backend is not None + self.task.pre_sim_step() + self._sim_backend.sim(self.task.sim_model, self.task.data, command) + self.task.post_sim_step() diff --git a/sumo/app/dora/simulation.py b/sumo/app/dora/simulation.py new file mode 100644 index 0000000..20c57cc --- /dev/null +++ b/sumo/app/dora/simulation.py @@ -0,0 +1,17 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.app.dora.simulation import SimulationNode as JudoSimulationNode + +import sumo.tasks # noqa: F401 -- register all sumo tasks +from sumo.app.dora.g1_simulation import G1Simulation + + +class SimulationNode(JudoSimulationNode): + """Simulation node with G1 backend support.""" + + def __init__(self, init_task: str = "spot_box_push", **kwargs) -> None: + kwargs.setdefault("custom_backends", {"mujoco_g1": G1Simulation}) + super().__init__(init_task=init_task, **kwargs) + + +__all__ = ["G1Simulation", "SimulationNode"] diff --git a/sumo/app/dora/visualization.py b/sumo/app/dora/visualization.py new file mode 100644 index 0000000..328ac25 --- /dev/null +++ b/sumo/app/dora/visualization.py @@ -0,0 +1,18 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.app.dora.visualization import VisualizationNode as JudoVisualizationNode + +import sumo.controller # noqa: F401 -- register controller/optimizer overrides +import sumo.tasks # noqa: F401 -- register all sumo tasks +from sumo.tasks import get_sumo_registered_tasks + + +class VisualizationNode(JudoVisualizationNode): + """Visualization node with sumo-only task choices.""" + + def __init__(self, **kwargs) -> None: + kwargs.setdefault("available_tasks", get_sumo_registered_tasks()) + super().__init__(**kwargs) + + +__all__ = ["VisualizationNode"] diff --git a/sumo/cli.py b/sumo/cli.py new file mode 100644 index 0000000..d0ea55c --- /dev/null +++ b/sumo/cli.py @@ -0,0 +1,53 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +import warnings +from pathlib import Path + +import hydra +from dora_utils.launch.run import run +from hydra import compose, initialize_config_dir +from hydra.core.config_store import ConfigStore + +# Importing judo.cli registers process cleanup handlers (atexit, signal, Popen wrapper) +from judo.cli import ( + CONFIG_PATH as JUDO_CONFIG_PATH, +) +from judo.cli import ( + _force_cleanup, + _warm_caches, +) +from omegaconf import DictConfig + +# suppress hydra warning +warnings.filterwarnings( + "ignore", + message=r".*Defaults list is missing `_self_`.*", + category=UserWarning, + module="hydra._internal.defaults_list", +) + +SUMO_CONFIG_PATH = (Path(__file__).parent / "configs").resolve() + + +@hydra.main(config_path=str(SUMO_CONFIG_PATH), config_name="sumo_default", version_base="1.3") +def _main(cfg: DictConfig) -> None: + try: + run(cfg) + except (KeyboardInterrupt, SystemExit): + _force_cleanup() + + +def app() -> None: + """Entry point for the sumo CLI.""" + import sumo.controller # noqa: F401 -- register controller/optimizer overrides + import sumo.tasks # noqa: F401 -- register all tasks + + _warm_caches() + + # Store judo's default config in ConfigStore so sumo_default.yaml can inherit it + cs = ConfigStore.instance() + with initialize_config_dir(config_dir=str(JUDO_CONFIG_PATH), version_base="1.3"): + default_cfg = compose(config_name="judo_dora_default") + cs.store("judo_dora", default_cfg) + + _main() diff --git a/sumo/configs/sumo_default.yaml b/sumo/configs/sumo_default.yaml new file mode 100644 index 0000000..19a654e --- /dev/null +++ b/sumo/configs/sumo_default.yaml @@ -0,0 +1,12 @@ +defaults: + - judo_dora + +task: spot_box_push + +node_definitions: + simulation: + _target_: sumo.app.dora.simulation.SimulationNode + visualization: + _target_: sumo.app.dora.visualization.VisualizationNode + controller: + _target_: sumo.app.dora.controller.ControllerNode diff --git a/sumo/controller/__init__.py b/sumo/controller/__init__.py new file mode 100644 index 0000000..d84400a --- /dev/null +++ b/sumo/controller/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from sumo.controller.controller import Controller, ControllerConfig, make_controller +from sumo.controller.optimizer_overrides import set_default_spot_optimizer_overrides +from sumo.controller.overrides import ( + set_default_g1_box_overrides, + set_default_g1_chair_push_overrides, + set_default_g1_door_overrides, + set_default_g1_table_push_overrides, + set_default_spot_overrides, +) + +# Register overrides on import +set_default_g1_box_overrides() +set_default_g1_door_overrides() +set_default_g1_chair_push_overrides() +set_default_g1_table_push_overrides() +set_default_spot_overrides() +set_default_spot_optimizer_overrides() + +__all__ = ["Controller", "ControllerConfig", "make_controller"] diff --git a/sumo/controller/controller.py b/sumo/controller/controller.py new file mode 100644 index 0000000..308f928 --- /dev/null +++ b/sumo/controller/controller.py @@ -0,0 +1,34 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""Controller with G1 rollout backend support.""" + +from __future__ import annotations + +from typing import Literal + +from judo.controller.controller import Controller, ControllerConfig, make_spline +from judo.controller.controller import make_controller as _judo_make_controller +from omegaconf import DictConfig + +from sumo.utils.mujoco import G1RolloutBackend + + +def make_controller( + init_task: str, + init_optimizer: str, + task_registration_cfg: DictConfig | None = None, + optimizer_registration_cfg: DictConfig | None = None, + rollout_backend: Literal["mujoco"] = "mujoco", +) -> Controller: + """Make a controller with G1 backend support.""" + return _judo_make_controller( + init_task=init_task, + init_optimizer=init_optimizer, + task_registration_cfg=task_registration_cfg, + optimizer_registration_cfg=optimizer_registration_cfg, + rollout_backend=rollout_backend, + custom_rollout_backends={"mujoco_g1": G1RolloutBackend}, + ) + + +__all__ = ["Controller", "ControllerConfig", "make_controller", "make_spline"] diff --git a/sumo/controller/optimizer_overrides.py b/sumo/controller/optimizer_overrides.py new file mode 100644 index 0000000..2bb6415 --- /dev/null +++ b/sumo/controller/optimizer_overrides.py @@ -0,0 +1,30 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.config import set_config_overrides +from judo.optimizers.base import OptimizerConfig +from judo.optimizers.cem import CrossEntropyMethodConfig +from judo.optimizers.mppi import MPPIConfig +from judo.optimizers.ps import PredictiveSamplingConfig + +from sumo.tasks import SPOT_TASK_NAMES + +_SPOT_OPTIMIZER_BASE = { + "num_rollouts": 24, + "num_nodes": 3, + "use_noise_ramp": True, + "noise_ramp": 3.5, +} + + +def _set_spot_optimizer_overrides(task_name: str) -> None: + """Sets the default optimizer config overrides for a Spot task.""" + set_config_overrides(task_name, OptimizerConfig, _SPOT_OPTIMIZER_BASE) + set_config_overrides(task_name, PredictiveSamplingConfig, _SPOT_OPTIMIZER_BASE) + set_config_overrides(task_name, CrossEntropyMethodConfig, {**_SPOT_OPTIMIZER_BASE, "num_elites": 3}) + set_config_overrides(task_name, MPPIConfig, _SPOT_OPTIMIZER_BASE) + + +def set_default_spot_optimizer_overrides() -> None: + """Sets the default task-specific optimizer config overrides for all Spot tasks.""" + for task_name in SPOT_TASK_NAMES: + _set_spot_optimizer_overrides(task_name) diff --git a/sumo/controller/overrides.py b/sumo/controller/overrides.py new file mode 100644 index 0000000..3883e28 --- /dev/null +++ b/sumo/controller/overrides.py @@ -0,0 +1,66 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.config import set_config_overrides +from judo.controller.controller import ControllerConfig + +from sumo.tasks import SPOT_TASK_NAMES + + +def set_default_g1_box_overrides() -> None: + """Sets the default task-specific controller config overrides for the g1 box task.""" + set_config_overrides( + "g1_box", + ControllerConfig, + { + "horizon": 2.5, + "spline_order": "cubic", + }, + ) + + +def set_default_g1_door_overrides() -> None: + """Sets the default task-specific controller config overrides for the g1 door task.""" + set_config_overrides( + "g1_door", + ControllerConfig, + { + "horizon": 2.5, + "spline_order": "cubic", + }, + ) + + +def set_default_g1_chair_push_overrides() -> None: + """Sets the default task-specific controller config overrides for the g1 chair push task.""" + set_config_overrides( + "g1_chair_push", + ControllerConfig, + { + "horizon": 2.5, + "spline_order": "cubic", + }, + ) + + +def set_default_g1_table_push_overrides() -> None: + """Sets the default task-specific controller config overrides for the g1 table push task.""" + set_config_overrides( + "g1_table_push", + ControllerConfig, + { + "horizon": 2.5, + "spline_order": "cubic", + }, + ) + + +def set_default_spot_overrides() -> None: + """Sets the default task-specific controller config overrides for all Spot tasks.""" + for task_name in SPOT_TASK_NAMES: + set_config_overrides( + task_name, + ControllerConfig, + { + "horizon": 2.0, + }, + ) diff --git a/sumo/models/meshes/barrier/crowd_barrier.obj b/sumo/models/meshes/barrier/crowd_barrier.obj new file mode 100644 index 0000000..9e436cc --- /dev/null +++ b/sumo/models/meshes/barrier/crowd_barrier.obj @@ -0,0 +1,210399 @@ +# Blender 4.3.2 +# www.blender.org +mtllib crowd.control.barrier.mesh.low.res.mtl +o APT.gate.mesh.low.res +v -0.474199 0.088291 -0.013797 +v -0.474683 0.079652 -0.030771 +v -0.477612 0.087678 -0.020202 +v -0.467498 0.086271 -0.003373 +v -0.469344 0.080353 -0.007624 +v 0.248147 0.820707 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-0.259003 0.362922 0.002745 +v -0.540701 0.399284 -0.013252 +v -0.462764 0.034426 -0.019658 +v -0.583571 0.196064 0.008878 +v 0.657602 0.736068 -0.014460 +v 0.065449 0.506253 -0.015635 +v 0.064993 0.506788 -0.007523 +v -0.802126 0.964612 0.012372 +v -0.795725 0.949604 0.012329 +v -0.867767 0.807384 0.013328 +v -0.039743 0.597031 -0.020097 +v -0.038414 0.594129 -0.012968 +v 0.497414 0.044711 -0.041333 +v 0.659890 0.477263 -0.014117 +v -0.666480 0.773247 0.010925 +v -0.660902 0.772918 0.011431 +v -0.298765 0.284268 0.009103 +v -0.779095 0.452371 0.002414 +v -0.778951 0.447910 -0.000466 +v -0.779363 0.443730 0.001946 +v -0.654829 0.638067 0.011597 +v -0.348982 0.573245 0.010438 +v 0.684111 0.759239 0.009702 +v 0.297283 0.196113 -0.019799 +v 0.500032 0.153104 -0.049385 +v 0.224888 0.263673 0.004526 +v 0.530796 0.300318 0.003931 +v -0.201447 0.444165 0.011596 +v 0.082290 0.462206 0.010298 +v 0.242130 0.506108 0.009641 +v 0.542526 0.638790 0.008146 +v 0.086091 0.709566 0.010446 +v 0.093896 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46006/400316/45999 45994/400317/45987 +f 45994/400318/45987 46006/400319/45999 66722/400320/66707 diff --git a/sumo/models/xml/g1/g1.xml b/sumo/models/xml/g1/g1.xml new file mode 100644 index 0000000..02962e3 --- /dev/null +++ b/sumo/models/xml/g1/g1.xml @@ -0,0 +1,312 @@ + + + + diff --git a/sumo/models/xml/g1/g1_box.xml b/sumo/models/xml/g1/g1_box.xml new file mode 100644 index 0000000..62d1dad --- /dev/null +++ b/sumo/models/xml/g1/g1_box.xml @@ -0,0 +1,26 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/g1/g1_box_carry.xml b/sumo/models/xml/g1/g1_box_carry.xml new file mode 100644 index 0000000..ec98d77 --- /dev/null +++ b/sumo/models/xml/g1/g1_box_carry.xml @@ -0,0 +1,61 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/g1/g1_chair.xml b/sumo/models/xml/g1/g1_chair.xml new file mode 100644 index 0000000..0c489b9 --- /dev/null +++ b/sumo/models/xml/g1/g1_chair.xml @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/g1/g1_door.xml b/sumo/models/xml/g1/g1_door.xml new file mode 100644 index 0000000..8aaab57 --- /dev/null +++ b/sumo/models/xml/g1/g1_door.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/g1/g1_table.xml b/sumo/models/xml/g1/g1_table.xml new file mode 100644 index 0000000..bb28757 --- /dev/null +++ b/sumo/models/xml/g1/g1_table.xml @@ -0,0 +1,52 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/barbell/barbell.xml b/sumo/models/xml/objects/barbell/barbell.xml new file mode 100644 index 0000000..dc48ff5 --- /dev/null +++ b/sumo/models/xml/objects/barbell/barbell.xml @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/crowd_barrier/crowd_barrier.xml b/sumo/models/xml/objects/crowd_barrier/crowd_barrier.xml new file mode 100644 index 0000000..f8a94fa --- /dev/null +++ b/sumo/models/xml/objects/crowd_barrier/crowd_barrier.xml @@ -0,0 +1,140 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/crowd_barrier/crowd_barrier_defs.xml b/sumo/models/xml/objects/crowd_barrier/crowd_barrier_defs.xml new file mode 100644 index 0000000..c79fd2e --- /dev/null +++ b/sumo/models/xml/objects/crowd_barrier/crowd_barrier_defs.xml @@ -0,0 +1,56 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + --> diff --git a/sumo/models/xml/objects/rugged_box/rugged_box.xml b/sumo/models/xml/objects/rugged_box/rugged_box.xml new file mode 100644 index 0000000..120f521 --- /dev/null +++ b/sumo/models/xml/objects/rugged_box/rugged_box.xml @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/table_short.xml b/sumo/models/xml/objects/table_short.xml new file mode 100644 index 0000000..23c432c --- /dev/null +++ b/sumo/models/xml/objects/table_short.xml @@ -0,0 +1,18 @@ + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/tire_rack/tire_rack.xml b/sumo/models/xml/objects/tire_rack/tire_rack.xml new file mode 100644 index 0000000..dda6d4e --- /dev/null +++ b/sumo/models/xml/objects/tire_rack/tire_rack.xml @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/tire_rack/tire_rack_defs.xml b/sumo/models/xml/objects/tire_rack/tire_rack_defs.xml new file mode 100644 index 0000000..a507a28 --- /dev/null +++ b/sumo/models/xml/objects/tire_rack/tire_rack_defs.xml @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/traffic_cone/traffic_cone.xml b/sumo/models/xml/objects/traffic_cone/traffic_cone.xml new file mode 100644 index 0000000..0df1fb8 --- /dev/null +++ b/sumo/models/xml/objects/traffic_cone/traffic_cone.xml @@ -0,0 +1,109 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/traffic_cone/traffic_cone_defs.xml b/sumo/models/xml/objects/traffic_cone/traffic_cone_defs.xml new file mode 100644 index 0000000..beff69b --- /dev/null +++ b/sumo/models/xml/objects/traffic_cone/traffic_cone_defs.xml @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/yellow_chair/yellow_chair.xml b/sumo/models/xml/objects/yellow_chair/yellow_chair.xml new file mode 100644 index 0000000..b70e3a3 --- /dev/null +++ b/sumo/models/xml/objects/yellow_chair/yellow_chair.xml @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/objects/yellow_chair/yellow_chair_defs.xml b/sumo/models/xml/objects/yellow_chair/yellow_chair_defs.xml new file mode 100644 index 0000000..d57059b --- /dev/null +++ b/sumo/models/xml/objects/yellow_chair/yellow_chair_defs.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_components/sensor.xml b/sumo/models/xml/spot_components/sensor.xml new file mode 100644 index 0000000..496f882 --- /dev/null +++ b/sumo/models/xml/spot_components/sensor.xml @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_barrier.xml b/sumo/models/xml/spot_tasks/spot_barrier.xml new file mode 100644 index 0000000..b64dc9b --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_barrier.xml @@ -0,0 +1,55 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_rugged_box.xml b/sumo/models/xml/spot_tasks/spot_rugged_box.xml new file mode 100644 index 0000000..5927766 --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_rugged_box.xml @@ -0,0 +1,56 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_table_short.xml b/sumo/models/xml/spot_tasks/spot_table_short.xml new file mode 100644 index 0000000..191fd8a --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_table_short.xml @@ -0,0 +1,33 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_tire.xml b/sumo/models/xml/spot_tasks/spot_tire.xml new file mode 100644 index 0000000..c6b61fa --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_tire.xml @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_tire_rack.xml b/sumo/models/xml/spot_tasks/spot_tire_rack.xml new file mode 100644 index 0000000..8642cfb --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_tire_rack.xml @@ -0,0 +1,63 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_traffic_cone.xml b/sumo/models/xml/spot_tasks/spot_traffic_cone.xml new file mode 100644 index 0000000..99af69f --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_traffic_cone.xml @@ -0,0 +1,51 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_two_tires.xml b/sumo/models/xml/spot_tasks/spot_two_tires.xml new file mode 100644 index 0000000..bf40936 --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_two_tires.xml @@ -0,0 +1,234 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_yellow_chair.xml b/sumo/models/xml/spot_tasks/spot_yellow_chair.xml new file mode 100644 index 0000000..81355c8 --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_yellow_chair.xml @@ -0,0 +1,47 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/models/xml/spot_tasks/spot_yellow_chair_ramp.xml b/sumo/models/xml/spot_tasks/spot_yellow_chair_ramp.xml new file mode 100644 index 0000000..60ea947 --- /dev/null +++ b/sumo/models/xml/spot_tasks/spot_yellow_chair_ramp.xml @@ -0,0 +1,37 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/run_mpc/__init__.py b/sumo/run_mpc/__init__.py new file mode 100644 index 0000000..536dbd9 --- /dev/null +++ b/sumo/run_mpc/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. diff --git a/sumo/run_mpc/__main__.py b/sumo/run_mpc/__main__.py new file mode 100644 index 0000000..d50ad4d --- /dev/null +++ b/sumo/run_mpc/__main__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +import tyro + +from sumo.run_mpc.run_mpc import RunMPCConfig, run_mpc + +run_mpc(tyro.cli(RunMPCConfig)) diff --git a/sumo/run_mpc/run_mpc.py b/sumo/run_mpc/run_mpc.py new file mode 100644 index 0000000..bce2fbb --- /dev/null +++ b/sumo/run_mpc/run_mpc.py @@ -0,0 +1,340 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""Headless MPC runner for benchmarking tasks.""" + +import inspect +import os +from dataclasses import dataclass + +import h5py +import numpy as np +import tyro +from judo.app.structs import MujocoState +from judo.optimizers import get_registered_optimizers +from judo.tasks import get_registered_tasks +from tqdm import tqdm + +import sumo.controller # noqa: F401 -- register controller/optimizer overrides +import sumo.tasks # noqa: F401 -- register all sumo tasks +from sumo.app.dora.g1_simulation import G1Simulation +from sumo.controller import Controller, ControllerConfig +from sumo.utils.extensions import require_g1_extensions, require_mujoco_extensions +from sumo.utils.mujoco import G1RolloutBackend + + +@dataclass +class RunMPCConfig: + """Config class for MPC runs.""" + + init_task: str = "spot_box_push" + init_optimizer: str = "cem" + visualize: bool = True + num_episodes: int = 2 + episode_length_s: float = 10.0 + save_results: bool = True + output_dir: str = "run_mpc/results" + verbose: bool = False + viz_dt: float = 0.02 + # Data recording options + record_all_data: bool = False + record_qvel: bool = False + record_xpos: bool = False + record_xquat: bool = False + record_ctrl: bool = False + record_sensordata: bool = False + record_mocap: bool = False + record_traces: bool = False + # Optimizer rollout recording options + record_rollouts: bool = True + record_rollout_controls: bool = False + record_rollout_sensors: bool = False + + +# --------------------------------------------------------------------------- +# Simulation backend resolution +# --------------------------------------------------------------------------- + + +def _create_sim(task_name: str): + """Create the right simulation backend for a task.""" + task_cls, _ = get_registered_tasks()[task_name] + default_backend = getattr(task_cls, "default_backend", None) + + if default_backend == "mujoco_g1": + require_g1_extensions() + return G1Simulation(init_task=task_name) + + if task_cls().uses_locomotion_policy: + require_mujoco_extensions() + from judo.simulation import get_simulation_backend + + return get_simulation_backend("mujoco_policy")(init_task=task_name) + + from judo.simulation import get_simulation_backend + + return get_simulation_backend("mujoco")(init_task=task_name) + + +def _make_condition_checker(method): + """Build a callable for success/failure that handles both G1 and Spot signatures.""" + if method is None: + return None + if "config" in inspect.signature(method).parameters: + return lambda model, data, config: method(model, data, config) + return lambda model, data, config: method(model, data) + + +# --------------------------------------------------------------------------- +# Single episode +# --------------------------------------------------------------------------- + + +def run_single_episode(config, task, controller, sim, viser_model=None, episode_idx=0): + """Run one episode, return recorded data.""" + sim_dt = task.sim_model.opt.timestep + plan_dt = 1.0 / controller.controller_cfg.control_freq + ctrl_dt = task.model.opt.timestep + num_steps = int(config.episode_length_s / sim_dt) + 1 + + steps_per_plan = max(1, int(plan_dt / sim_dt)) + steps_per_ctrl = max(1, int(ctrl_dt / sim_dt)) + steps_per_record = max(1, int(config.viz_dt / sim_dt)) + + # Reset + np.random.seed(episode_idx) + task.reset() + controller.reset() + task.data.time = 0.0 + + check_success = _make_condition_checker(getattr(task, "success", None)) + check_failure = _make_condition_checker(getattr(task, "failure", None)) + should_record = lambda field: config.record_all_data or getattr(config, f"record_{field}", False) + + data = { + "rewards": [], + "qpos_traj": [], + "time_traj": [], + "success": False, + "failure": False, + "length": 0.0, + } + if should_record("qvel"): + data["qvel_traj"] = [] + if should_record("xpos"): + data["xpos_traj"] = [] + if should_record("xquat"): + data["xquat_traj"] = [] + if should_record("ctrl"): + data["ctrl_traj"] = [] + if should_record("sensordata"): + data["sensordata_traj"] = [] + if should_record("mocap"): + data["mocap_pos_traj"] = [] + data["mocap_quat_traj"] = [] + if should_record("traces"): + data["traces_traj"] = [] + if should_record("rollouts"): + data["rollout_states"] = [] + data["rollout_rewards"] = [] + if should_record("rollout_controls"): + data["rollout_controls"] = [] + if should_record("rollout_sensors"): + data["rollout_sensors"] = [] + + current_action = None + curr_time = 0.0 + + for step in tqdm(range(num_steps), desc=f"Episode {episode_idx + 1}/{config.num_episodes}", leave=False): + curr_time = step * sim_dt + + # Plan + if step % steps_per_plan == 0: + controller.update_states( + MujocoState( + time=curr_time, + qpos=np.array(task.data.qpos), + qvel=np.array(task.data.qvel), + xpos=np.array(task.data.xpos), + xquat=np.array(task.data.xquat), + mocap_pos=np.array(task.data.mocap_pos), + mocap_quat=np.array(task.data.mocap_quat), + sim_metadata={}, + ) + ) + controller.update_action() + data["rewards"].append(float(controller.rewards.max())) + + if should_record("rollouts"): + data["rollout_states"].append(np.array(controller.states)) + data["rollout_rewards"].append(np.array(controller.rewards)) + if should_record("rollout_controls"): + data["rollout_controls"].append(np.array(controller.rollout_controls)) + if should_record("rollout_sensors"): + data["rollout_sensors"].append(np.array(controller.sensors)) + + # Control + if step % steps_per_ctrl == 0: + current_action = controller.action(curr_time) + + # Step simulation + if current_action is not None: + sim.step(current_action) + controller.system_metadata = task.get_sim_metadata() + task.post_sim_step() + + # Check termination + sim_model = task.sim_model + if check_success is not None: + data["success"] = data["success"] or check_success(sim_model, task.data, task.config) + if check_failure is not None: + data["failure"] = data["failure"] or check_failure(sim_model, task.data, task.config) + + # Update visualization at real-time rate + if viser_model is not None and step % steps_per_record == 0: + viser_model.set_data(task.data) + # time.sleep(config.viz_dt) + + # Record trajectory + if step % steps_per_record == 0: + data["time_traj"].append(curr_time) + data["qpos_traj"].append(np.array(task.data.qpos)) + if should_record("qvel"): + data["qvel_traj"].append(np.array(task.data.qvel)) + if should_record("xpos"): + data["xpos_traj"].append(np.array(task.data.xpos)) + if should_record("xquat"): + data["xquat_traj"].append(np.array(task.data.xquat)) + if should_record("ctrl"): + data["ctrl_traj"].append(np.array(task.data.ctrl)) + if should_record("sensordata"): + data["sensordata_traj"].append(np.array(task.data.sensordata)) + if should_record("mocap"): + data["mocap_pos_traj"].append(np.array(task.data.mocap_pos)) + data["mocap_quat_traj"].append(np.array(task.data.mocap_quat)) + if should_record("traces") and controller.traces is not None: + data["traces_traj"].append(np.array(controller.traces)) + + data["length"] = curr_time + for key, val in data.items(): + if isinstance(val, list): + data[key] = np.asarray(val) if val else np.empty((0,)) + return data + + +# --------------------------------------------------------------------------- +# Main entry point +# --------------------------------------------------------------------------- + + +def run_mpc(config: RunMPCConfig) -> list[dict]: + """Set up and run MPC for multiple episodes.""" + task_dict = get_registered_tasks() + if config.init_task not in task_dict: + raise ValueError(f"Task '{config.init_task}' is not registered.") + + # Create simulation backend (handles G1, Spot locomotion policy, plain MuJoCo) + sim = _create_sim(config.init_task) + task = sim.task + + # Create optimizer + optimizer_dict = get_registered_optimizers() + if config.init_optimizer not in optimizer_dict: + raise ValueError(f"Optimizer '{config.init_optimizer}' is not registered.") + optimizer_cls, optimizer_config_cls = optimizer_dict[config.init_optimizer] + optimizer_config = optimizer_config_cls() + optimizer_config.set_override(config.init_task) + optimizer = optimizer_cls(optimizer_config, task.nu) + + # Create controller + controller_config = ControllerConfig() + controller_config.set_override(config.init_task) + controller = Controller(controller_config, task, optimizer, custom_rollout_backends={"mujoco_g1": G1RolloutBackend}) + + # Set up visualization + viser_model = None + if config.visualize: + from dataclasses import fields as dc_fields + + import viser + from judo.visualizers.model import ViserMjModel + + server = viser.ViserServer() + viser_model = ViserMjModel(server, task.spec, geom_exclude_substring="collision") + viser_model.set_data(task.data) + + # Render goal position markers from task config metadata + for f in dc_fields(task.config): + ui_cfg = f.metadata.get("ui_array_config") if f.metadata else None + vis = ui_cfg.get("vis") if ui_cfg else None + if vis is None: + continue + goal_val = np.asarray(getattr(task.config, f.name)) + xyz_indices = vis["xyz_vis_indices"] + xyz_defaults = vis.get("xyz_vis_defaults", (0.0, 0.0, 0.0)) + position = np.array( + [ + goal_val[xyz_indices[i]] if xyz_indices[i] is not None else xyz_defaults[i] + for i in range(len(xyz_indices)) + ] + ) + server.scene.add_icosphere(vis["name"], radius=0.15, color=(0.0, 0.0, 1.0), position=position) + + print("Visualizer running at: http://localhost:8080") + + # Run episodes + all_episodes = [] + for i in range(config.num_episodes): + episode = run_single_episode(config, task, controller, sim, viser_model=viser_model, episode_idx=i) + all_episodes.append(episode) + + # Print summary + avg_reward = np.mean([np.mean(ep["rewards"]) for ep in all_episodes]) + num_successes = sum(ep["success"] for ep in all_episodes) + num_failures = sum(ep["failure"] for ep in all_episodes) + avg_length = np.mean([ep["length"] for ep in all_episodes]) + + print(f"\n{'=' * 60}") + print(f"Task: {config.init_task} Optimizer: {config.init_optimizer}") + print(f"Episodes: {config.num_episodes} Length: {avg_length:.1f}s") + print( + f"Avg Reward: {avg_reward:.2f} Success: {num_successes}/{config.num_episodes} Failure: {num_failures}/{config.num_episodes}" + ) + print(f"{'=' * 60}") + + # Save results + if config.save_results: + _save_results(config, all_episodes, task) + + return all_episodes + + +def _save_results(config: RunMPCConfig, all_episodes: list[dict], task) -> None: + """Save episode data to HDF5.""" + output_dir = os.path.join(config.output_dir, config.init_task) + os.makedirs(output_dir, exist_ok=True) + h5_path = os.path.join(output_dir, "trajectories.h5") + + with h5py.File(h5_path, "w") as f: + f.attrs["task"] = config.init_task + f.attrs["optimizer"] = config.init_optimizer + f.attrs["num_episodes"] = config.num_episodes + f.attrs["episode_length_s"] = config.episode_length_s + f.attrs["sim_dt"] = task.sim_model.opt.timestep + + task_group = f.create_group(config.init_task) + opt_group = task_group.create_group(config.init_optimizer) + + for i, ep in enumerate(all_episodes): + g = opt_group.create_group(f"episode_{i}") + g.attrs["success"] = ep["success"] + g.attrs["failure"] = ep["failure"] + g.attrs["length"] = ep["length"] + for key, val in ep.items(): + if isinstance(val, np.ndarray) and val.size > 0: + g.create_dataset(key, data=val) + + print(f"Results saved to: {h5_path}") + + +if __name__ == "__main__": + run_mpc(tyro.cli(RunMPCConfig)) diff --git a/sumo/tasks/__init__.py b/sumo/tasks/__init__.py new file mode 100644 index 0000000..6749feb --- /dev/null +++ b/sumo/tasks/__init__.py @@ -0,0 +1,82 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.tasks import get_registered_tasks, register_task + +G1_TASK_NAMES = ( + "g1_base", + "g1_box", + "g1_chair_push", + "g1_door", + "g1_table_push", +) + +from sumo.tasks.g1.g1_base import G1Base, G1BaseConfig +from sumo.tasks.g1.g1_box import G1Box, G1BoxConfig +from sumo.tasks.g1.g1_chair_push import G1ChairPush, G1ChairPushConfig +from sumo.tasks.g1.g1_door import G1Door, G1DoorConfig +from sumo.tasks.g1.g1_table_push import G1TablePush, G1TablePushConfig + +register_task("g1_base", G1Base, G1BaseConfig) +register_task("g1_box", G1Box, G1BoxConfig) +register_task("g1_chair_push", G1ChairPush, G1ChairPushConfig) +register_task("g1_door", G1Door, G1DoorConfig) +register_task("g1_table_push", G1TablePush, G1TablePushConfig) + +SPOT_TASK_NAMES = ( + "spot_base", + "spot_box_push", + "spot_chair_push", + "spot_cone_push", + "spot_rack_push", + "spot_tire_push", + "spot_cone_upright", + "spot_chair_ramp", + "spot_barrier_upright", + "spot_barrier_drag", + "spot_tire_roll", + "spot_tire_upright", + "spot_tire_stack", + "spot_tire_rack_drag", + "spot_rugged_box_push", +) + +# Spot tasks (advanced, using C++ ONNX rollout backend via judo-rai's mujoco_spot) +from sumo.tasks.spot.spot_barrier_drag import SpotBarrierDrag, SpotBarrierDragConfig +from sumo.tasks.spot.spot_barrier_upright import SpotBarrierUpright, SpotBarrierUprightConfig +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_box_push import SpotBoxPush, SpotBoxPushConfig +from sumo.tasks.spot.spot_chair_push import SpotChairPush, SpotChairPushConfig +from sumo.tasks.spot.spot_chair_ramp import SpotChairRamp, SpotChairRampConfig +from sumo.tasks.spot.spot_cone_push import SpotConePush, SpotConePushConfig +from sumo.tasks.spot.spot_cone_upright import SpotConeUpright, SpotConeUprightConfig +from sumo.tasks.spot.spot_rack_push import SpotRackPush, SpotRackPushConfig +from sumo.tasks.spot.spot_rugged_box_push import SpotRuggedBoxPush, SpotRuggedBoxPushConfig +from sumo.tasks.spot.spot_tire_push import SpotTirePush, SpotTirePushConfig +from sumo.tasks.spot.spot_tire_rack_drag import SpotTireRackDrag, SpotTireRackDragConfig +from sumo.tasks.spot.spot_tire_roll import SpotTireRoll, SpotTireRollConfig +from sumo.tasks.spot.spot_tire_stack import SpotTireStack, SpotTireStackConfig +from sumo.tasks.spot.spot_tire_upright import SpotTireUpright, SpotTireUprightConfig + +register_task("spot_base", SpotBase, SpotBaseConfig) +register_task("spot_box_push", SpotBoxPush, SpotBoxPushConfig) +register_task("spot_chair_push", SpotChairPush, SpotChairPushConfig) +register_task("spot_cone_push", SpotConePush, SpotConePushConfig) +register_task("spot_rack_push", SpotRackPush, SpotRackPushConfig) +register_task("spot_tire_push", SpotTirePush, SpotTirePushConfig) +register_task("spot_cone_upright", SpotConeUpright, SpotConeUprightConfig) +register_task("spot_chair_ramp", SpotChairRamp, SpotChairRampConfig) +register_task("spot_barrier_upright", SpotBarrierUpright, SpotBarrierUprightConfig) +register_task("spot_barrier_drag", SpotBarrierDrag, SpotBarrierDragConfig) +register_task("spot_tire_roll", SpotTireRoll, SpotTireRollConfig) +register_task("spot_tire_upright", SpotTireUpright, SpotTireUprightConfig) +register_task("spot_tire_stack", SpotTireStack, SpotTireStackConfig) +register_task("spot_tire_rack_drag", SpotTireRackDrag, SpotTireRackDragConfig) +register_task("spot_rugged_box_push", SpotRuggedBoxPush, SpotRuggedBoxPushConfig) + +SUMO_TASK_NAMES = G1_TASK_NAMES + SPOT_TASK_NAMES + + +def get_sumo_registered_tasks() -> dict[str, tuple[type, type]]: + """Return only the task registrations owned by sumo.""" + registered_tasks = get_registered_tasks() + return {task_name: registered_tasks[task_name] for task_name in SUMO_TASK_NAMES if task_name in registered_tasks} diff --git a/sumo/tasks/g1/__init__.py b/sumo/tasks/g1/__init__.py new file mode 100644 index 0000000..536dbd9 --- /dev/null +++ b/sumo/tasks/g1/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. diff --git a/sumo/tasks/g1/g1_base.py b/sumo/tasks/g1/g1_base.py new file mode 100644 index 0000000..99b3b4e --- /dev/null +++ b/sumo/tasks/g1/g1_base.py @@ -0,0 +1,346 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any, Generic, TypeVar + +import mujoco +import numpy as np +from judo.tasks.base import Task, TaskConfig + +from sumo import MODEL_PATH +from sumo.utils.indexing import get_pos_indices + +XML_PATH = str(MODEL_PATH / "xml/g1/g1.xml") + +# Default joint positions for G1 (29 joints total) +# Order: left_leg(6) + right_leg(6) + waist(3) + left_arm(7) + right_arm(7) +DEFAULT_JOINT_POSITIONS = np.array( + [ + -0.312, + 0.0, + 0.0, + 0.669, + -0.363, + 0.0, # Left leg (6) + -0.312, + 0.0, + 0.0, + 0.669, + -0.363, + 0.0, # Right leg (6) + 0.0, + 0.0, + 0.0, # Waist (3) + 0.2, + 0.2, + 0.0, + 0.6, + 0.0, + 0.0, + 0.0, # Left arm (7) + 0.2, + -0.2, + 0.0, + 0.6, + 0.0, + 0.0, + 0.0, # Right arm (7) + ] +) + +# Extract default positions for each arm and wrist +# Arm = shoulder (3) + elbow (1) = 4 joints +# Wrist = 3 joints +LEFT_ARM_DEFAULT_POS = DEFAULT_JOINT_POSITIONS[15:19] # 4 joints (shoulder + elbow) +LEFT_WRIST_DEFAULT_POS = DEFAULT_JOINT_POSITIONS[19:22] # 3 joints (wrist) +RIGHT_ARM_DEFAULT_POS = DEFAULT_JOINT_POSITIONS[22:26] # 4 joints (shoulder + elbow) +RIGHT_WRIST_DEFAULT_POS = DEFAULT_JOINT_POSITIONS[26:29] # 3 joints (wrist) + +# Joint names for reference +LEFT_ARM_JOINT_NAMES = ["left_shoulder_pitch", "left_shoulder_roll", "left_shoulder_yaw", "left_elbow"] +LEFT_WRIST_JOINT_NAMES = ["left_wrist_roll", "left_wrist_pitch", "left_wrist_yaw"] +RIGHT_ARM_JOINT_NAMES = ["right_shoulder_pitch", "right_shoulder_roll", "right_shoulder_yaw", "right_elbow"] +RIGHT_WRIST_JOINT_NAMES = ["right_wrist_roll", "right_wrist_pitch", "right_wrist_yaw"] + +# Base velocity limits +LIN_VEL_X_RANGE = [-0.25, 0.25] +LIN_VEL_Y_RANGE = [-0.25, 0.25] +ANG_VEL_z_RANGE = [-0.1, 0.1] +STANDING_HEIGHT = 0.76 + + +@dataclass +class G1BaseConfig(TaskConfig): + """Base config for G1 tasks.""" + + fall_penalty: float = 2500.0 + fall_threshold: float = 0.6 + pass + + +ConfigT = TypeVar("ConfigT", bound=G1BaseConfig) + + +class G1Base(Task[ConfigT], Generic[ConfigT]): + """Base class for G1 tasks.""" + + config_t: type[G1BaseConfig] = G1BaseConfig # type: ignore[assignment] + default_backend = "mujoco_g1" # Use G1-specific backend + + def _process_spec(self) -> None: + """No-op for G1 tasks (meshes are local).""" + pass + + def __init__( + self, + model_path: str = XML_PATH, + use_left_arm: bool = False, + use_right_arm: bool = False, + use_left_wrist: bool = False, + use_right_wrist: bool = False, + ) -> None: + super().__init__(model_path) + self.use_left_arm = use_left_arm + self.use_right_arm = use_right_arm + self.use_left_wrist = use_left_wrist + self.use_right_wrist = use_right_wrist + + self.body_pose_idx = get_pos_indices(self.model, "floating_base_joint") + + # Extract joint control ranges from the model + self._extract_joint_limits() + + # Set default command based on arm usage + self._set_default_command() + + def _set_default_command(self) -> None: + """Set default command based on arm and wrist usage. + + Default command structure: + - Base velocities: [0, 0, 0] (always present) + - Left arm (if used): 4 default positions (shoulder + elbow) + - Left wrist (if used): 3 default positions + - Right arm (if used): 4 default positions (shoulder + elbow) + - Right wrist (if used): 3 default positions + """ + base_vel = np.array([0.0, 0.0, 0.0]) + parts = [base_vel] + + if self.use_left_arm: + parts.append(LEFT_ARM_DEFAULT_POS) + if self.use_left_wrist: + parts.append(LEFT_WRIST_DEFAULT_POS) + if self.use_right_arm: + parts.append(RIGHT_ARM_DEFAULT_POS) + if self.use_right_wrist: + parts.append(RIGHT_WRIST_DEFAULT_POS) + + self.default_command = np.concatenate(parts) + + def _extract_joint_limits(self) -> None: + """Extract joint position limits from the MuJoCo model actuators.""" + # Get actuator control ranges + # Order in model: legs(12) + waist(3) + left_arm(4) + left_wrist(3) + right_arm(4) + right_wrist(3) = 29 + self.joint_ctrl_lower = self.model.actuator_ctrlrange[:, 0] + self.joint_ctrl_upper = self.model.actuator_ctrlrange[:, 1] + + # Split by joint groups (based on actuator order in XML) + # Left leg: 0-5, Right leg: 6-11, Waist: 12-14 + # Left arm (shoulder+elbow): 15-18, Left wrist: 19-21 + # Right arm (shoulder+elbow): 22-25, Right wrist: 26-28 + self.left_arm_lower = self.joint_ctrl_lower[15:19] + self.left_arm_upper = self.joint_ctrl_upper[15:19] + self.left_wrist_lower = self.joint_ctrl_lower[19:22] + self.left_wrist_upper = self.joint_ctrl_upper[19:22] + self.right_arm_lower = self.joint_ctrl_lower[22:26] + self.right_arm_upper = self.joint_ctrl_upper[22:26] + self.right_wrist_lower = self.joint_ctrl_lower[26:29] + self.right_wrist_upper = self.joint_ctrl_upper[26:29] + + @property + def nu(self) -> int: + """Number of controls for this task. + + Base velocity: 3 (always) + Left arm: 4 (shoulder + elbow) + Left wrist: 3 + Right arm: 4 (shoulder + elbow) + Right wrist: 3 + """ + count = 3 # Base velocity always present + + if self.use_left_arm: + count += 4 + if self.use_left_wrist: + count += 3 + if self.use_right_arm: + count += 4 + if self.use_right_wrist: + count += 3 + + return count + + @property + def ctrlrange(self) -> np.ndarray: + """Control bounds for this task. + + Returns control bounds based on arm/wrist usage: + - Base velocity: [lin_vel_x, lin_vel_y, ang_vel_z] - always included + - Left arm (if used): 4 joint position commands (shoulder + elbow) + - Left wrist (if used): 3 joint position commands + - Right arm (if used): 4 joint position commands (shoulder + elbow) + - Right wrist (if used): 3 joint position commands + + Returns: + Array of shape (nu, 2) where each row is [lower, upper] bound + """ + # Base velocity limits + base_lower = np.array([LIN_VEL_X_RANGE[0], LIN_VEL_Y_RANGE[0], ANG_VEL_z_RANGE[0]]) + base_upper = np.array([LIN_VEL_X_RANGE[1], LIN_VEL_Y_RANGE[1], ANG_VEL_z_RANGE[1]]) + + # Build control bounds based on what's being used + lower_parts = [base_lower] + upper_parts = [base_upper] + + if self.use_left_arm: + lower_parts.append(self.left_arm_lower) + upper_parts.append(self.left_arm_upper) + if self.use_left_wrist: + lower_parts.append(self.left_wrist_lower) + upper_parts.append(self.left_wrist_upper) + if self.use_right_arm: + lower_parts.append(self.right_arm_lower) + upper_parts.append(self.right_arm_upper) + if self.use_right_wrist: + lower_parts.append(self.right_wrist_lower) + upper_parts.append(self.right_wrist_upper) + + lower_bound = np.concatenate(lower_parts) + upper_bound = np.concatenate(upper_parts) + + return np.stack([lower_bound, upper_bound], axis=-1) + + @property + def actuator_ctrlrange(self) -> np.ndarray: + """Control bounds in task-control space. + + G1 tasks optimize compact task controls, not the raw 29 actuator commands. + """ + return self.ctrlrange + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for G1 tasks with falling penalty. + + Args: + states: Array of shape (batch_size, horizon+1, nq+nv) + sensors: Array of shape (batch_size, horizon, nsensordata) + controls: Array of shape (batch_size, horizon, nu) + system_metadata: Optional system metadata + + Returns: + Array of shape (batch_size,) with rewards for each rollout + """ + batch_size = states.shape[0] + config = self.config + + # Extract qpos from states + qpos = states[..., : self.model.nq] + + # Get body height across all timesteps + body_height = qpos[..., self.body_pose_idx[2]] + + # Check if any state in the rollout has the robot fallen + # Returns -fall_penalty if fallen at any point, 0 otherwise + has_fallen = (body_height <= config.fall_threshold).any(axis=-1) + fallen_reward = -config.fall_penalty * np.asarray(has_fallen) + + assert fallen_reward.shape == (batch_size,), f"Expected shape ({batch_size},), got {fallen_reward.shape}" + return fallen_reward + + def task_to_sim_ctrl(self, controls: np.ndarray) -> np.ndarray: + """Map compact controls (..., nu) to 17-dim command expected by C++ rollout. + + Layout of 17-dim command: + [0:3] base_vel_cmd (vx, vy, vyaw) + [3:7] left_arm_cmd (4 joints: shoulder + elbow) + [7:10] left_wrist_cmd (3 joints) + [10:14] right_arm_cmd (4 joints: shoulder + elbow) + [14:17] right_wrist_cmd (3 joints) + + If arm/wrist components are not used, their commands are set to 0, which tells + the C++ backend to use the policy output for those joints. + """ + controls = np.asarray(controls) + added_dim = False + if controls.ndim == 1: + controls = controls[None] + added_dim = True + + T = controls.shape[1] if controls.ndim == 3 else 1 + if controls.ndim == 2: + # assume (..., nu) at sim timestep grid + controls = controls[:, None, :] + T = 1 + + # Always create 17-dim output + out = np.zeros((controls.shape[0], controls.shape[1], 17), dtype=controls.dtype) + + # Base velocity (always present) + out[..., 0:3] = controls[..., 0:3] + + # Map arm and wrist controls based on what's enabled + # Track current position in the input controls + idx = 3 # Start after base velocity + + if self.use_left_arm: + out[..., 3:7] = controls[..., idx : idx + 4] # Left arm (4 joints) + idx += 4 + if self.use_left_wrist: + out[..., 7:10] = controls[..., idx : idx + 3] # Left wrist (3 joints) + idx += 3 + if self.use_right_arm: + out[..., 10:14] = controls[..., idx : idx + 4] # Right arm (4 joints) + idx += 4 + if self.use_right_wrist: + out[..., 14:17] = controls[..., idx : idx + 3] # Right wrist (3 joints) + idx += 3 + + # Remove dimensions we added + if T == 1 and out.ndim == 3: + out = out.squeeze(1) + if added_dim: + out = out.squeeze(0) + + return out + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot. + + Returns: + Array of qpos values: [base_pos(3), base_quat(4), joint_pos(29)] + """ + return np.array( + [ + 0.0, + 0.0, + STANDING_HEIGHT, # Base position (x, y, z) + 1.0, + 0.0, + 0.0, + 0.0, # Base orientation (quaternion: w, x, y, z) + *DEFAULT_JOINT_POSITIONS, # All joint positions (29) + ] + ) + + def reset(self) -> None: + """Reset the robot to its default standing pose.""" + self.data.qpos = self.reset_pose + self.data.qvel = np.zeros_like(self.data.qvel) + mujoco.mj_forward(self.model, self.data) diff --git a/sumo/tasks/g1/g1_box.py b/sumo/tasks/g1/g1_box.py new file mode 100644 index 0000000..fde2b6f --- /dev/null +++ b/sumo/tasks/g1/g1_box.py @@ -0,0 +1,209 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.g1.g1_base import DEFAULT_JOINT_POSITIONS, STANDING_HEIGHT, G1Base, G1BaseConfig +from sumo.utils.indexing import get_pos_indices, get_sensor_indices, get_vel_indices + +XML_PATH = str(MODEL_PATH / "xml/g1/g1_box.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +RESET_OBJECT_POSE = np.array([3, 0, 0.275, 1, 0, 0, 0]) +# Annulus object position sampling +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 + +# Success condition tolerances +POSITION_TOLERANCE = 0.3 +VELOCITY_TOLERANCE = 0.1 + +# Arm usage configuration for box manipulation +USE_LEFT_ARM = False +USE_RIGHT_ARM = False +USE_LEFT_WRIST = False +USE_RIGHT_WRIST = False + + +@dataclass +class G1BoxConfig(G1BaseConfig): + """Config for the G1 box manipulation task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, 0.0], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 1.0], + steps=[0.1, 0.1, 0.05], + vis_name="box_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[0.0, 0.0, 0.0], + ) + w_goal: float = 50.0 + w_orientation: float = 15.0 + w_pelvis_proximity: float = 0.1 + w_hand_proximity: float = 10.0 + w_robot_orientation: float = 50.0 + w_controls: float = 3 + + +class G1Box(G1Base): + """Task getting G1 to move a box to a desired goal location. + + Uses right arm for manipulation by default (USE_RIGHT_ARM=True). + """ + + config_t = G1BoxConfig + config: G1BoxConfig + + def __init__(self, model_path: str = XML_PATH) -> None: + super().__init__( + model_path=model_path, + use_left_arm=USE_LEFT_ARM, + use_right_arm=USE_RIGHT_ARM, + use_left_wrist=USE_LEFT_WRIST, + use_right_wrist=USE_RIGHT_WRIST, + ) + self.body_pose_idx = get_pos_indices(self.model, "floating_base_joint") + self.object_pose_idx = get_pos_indices(self.model, ["box_joint"]) + self.object_vel_idx = get_vel_indices(self.model, ["box_joint"]) + self.object_y_axis_idx = get_sensor_indices(self.model, "object_y_axis") + self.left_palm_to_object_idx = get_sensor_indices(self.model, "trace_left_palm") + self.right_palm_to_object_idx = get_sensor_indices(self.model, "trace_right_palm") + + @property + def nu(self) -> int: + """Number of controls for this task.""" + return super().nu + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the G1 box moving task.""" + batch_size = states.shape[0] + config = self.config + + qpos = states[..., : self.model.nq] + body_height = qpos[..., self.body_pose_idx[2]] + body_pos = qpos[..., self.body_pose_idx[0:3]] + object_pos = qpos[..., self.object_pose_idx[0:3]] + + object_y_axis = sensors[..., self.object_y_axis_idx] + left_palm_to_object = sensors[..., self.left_palm_to_object_idx] + right_palm_to_object = sensors[..., self.right_palm_to_object_idx] + + # Check if any state in the rollout has G1 fallen + g1_fallen_reward = -config.fall_penalty * (body_height <= config.fall_threshold).any(axis=-1) + + # Compute l2 distance from object pos. to goal. + goal_reward = -config.w_goal * np.linalg.norm( + object_pos - np.array(config.goal_position)[None, None], axis=-1 + ).mean(-1) + + # Box orientation reward - penalize if box is not upright + box_orientation_reward = -config.w_orientation * np.abs(np.dot(object_y_axis, Z_AXIS) - 1.0).sum(axis=-1) + + # Compute l2 distance from pelvis pos. to object pos. + pelvis_proximity_reward = config.w_pelvis_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + # Compute l2 distance from hands to object - encourage getting hands close to box + # Use the minimum distance from either hand + left_hand_dist = np.linalg.norm(left_palm_to_object, axis=-1) + right_hand_dist = np.linalg.norm(right_palm_to_object, axis=-1) + min_hand_dist = np.minimum(left_hand_dist, right_hand_dist) + hand_proximity_reward = -config.w_hand_proximity * min_hand_dist.mean(-1) + + # Control cost: penalize velocity commands (absolute) and arm position deviations (from default) + base_vel = controls[..., :3] + vel_cost = np.linalg.norm(base_vel, axis=-1).mean(-1) + + body_quat = qpos[..., self.body_pose_idx[3:7]] # (batch, T, 4) - quaternion [w, x, y, z] + + # Calculate forward direction (X-axis) from quaternion + # For a quaternion [w, x, y, z], the rotated forward vector (originally [1, 0, 0]) is: + # forward_x = 1 - 2(y^2 + z^2) + y_q = body_quat[..., 2] + z_q = body_quat[..., 3] + forward_x = 1 - 2 * (y_q**2 + z_q**2) + + # Reward when forward_x is positive (facing +X direction, forward) + # forward_x = 1 means perfectly facing forward (default orientation) + robot_orientation_reward = config.w_robot_orientation * forward_x.mean(-1) + + if self.use_left_arm or self.use_right_arm: + # Penalize arm positions as deviation from default + arm_commands = controls[..., 3:] + arm_defaults = self.default_command[3:] + arm_deviation = arm_commands - arm_defaults + arm_cost = np.linalg.norm(arm_deviation, axis=-1).mean(-1) + controls_reward = -config.w_controls * (vel_cost + arm_cost) + else: + controls_reward = -config.w_controls * vel_cost + + assert g1_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert box_orientation_reward.shape == (batch_size,) + assert pelvis_proximity_reward.shape == (batch_size,) + assert hand_proximity_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + assert robot_orientation_reward.shape == (batch_size,) + + return ( + g1_fallen_reward + + goal_reward + + box_orientation_reward + + pelvis_proximity_reward + + hand_proximity_reward + + robot_orientation_reward + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + # Sample object position in an annulus around the robot + + object_pos = np.array([-1.0, 0]) # + np.random.randn(2) * 0.1 + reset_object_pose = np.array([*object_pos, 0.5, 1, 0, 0, 0]) + + # G1 reset pose: base position + orientation + joint positions + object pose + return np.array( + [ + -2.0, + 0.0, + STANDING_HEIGHT, # Standing height + 1, + 0, + 0, + 0, # Quaternion (upright orientation) + *DEFAULT_JOINT_POSITIONS, # Joint positions + *reset_object_pose, # Object pose + ] + ) + + def success( + self, model: MjModel, data: MjData, config: G1BoxConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if the box is in the goal position.""" + object_pos = data.qpos[..., self.object_pose_idx[0:2]] # XY only + object_vel = data.qvel[..., self.object_vel_idx[0:3]] + goal_pos = np.array(config.goal_position[:2]) # XY only + position_check = np.linalg.norm(object_pos - goal_pos, axis=-1, ord=np.inf) < POSITION_TOLERANCE + velocity_check = np.linalg.norm(object_vel, axis=-1) < VELOCITY_TOLERANCE + return position_check and velocity_check + + def failure( + self, model: MjModel, data: MjData, config: G1BoxConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if G1 has fallen.""" + body_height = data.qpos[..., self.body_pose_idx[2]] + return bool(body_height <= config.fall_threshold) diff --git a/sumo/tasks/g1/g1_chair_push.py b/sumo/tasks/g1/g1_chair_push.py new file mode 100644 index 0000000..7df9429 --- /dev/null +++ b/sumo/tasks/g1/g1_chair_push.py @@ -0,0 +1,203 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.g1.g1_base import DEFAULT_JOINT_POSITIONS, STANDING_HEIGHT, G1Base, G1BaseConfig +from sumo.utils.indexing import get_pos_indices, get_sensor_indices, get_vel_indices + +XML_PATH = str(MODEL_PATH / "xml/g1/g1_chair.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +CHAIR_HEIGHT = 0.0 # Height of the chair base from the ground +# Annulus object position sampling +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 + +# Success condition tolerances +POSITION_TOLERANCE = 0.3 +VELOCITY_TOLERANCE = 0.1 + +# Arm usage configuration for chair manipulation +USE_LEFT_ARM = False +USE_RIGHT_ARM = False +USE_LEFT_WRIST = False +USE_RIGHT_WRIST = False + + +@dataclass +class G1ChairPushConfig(G1BaseConfig): + """Config for the G1 chair push task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, CHAIR_HEIGHT], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 1.0], + vis_name="goal_position", + xyz_vis_indices=[0, 1, None], + ) + w_goal: float = 30.0 + w_orientation: float = 15.0 + w_pelvis_proximity: float = 0.1 + w_hand_proximity: float = 10.0 + w_object_velocity: float = 10.0 + w_controls: float = 3 + + +class G1ChairPush(G1Base): + config_t = G1ChairPushConfig + config: G1ChairPushConfig + + """Task getting G1 to push a yellow chair to a desired goal location. + + Uses both arms for manipulation by default (USE_LEFT_ARM=True, USE_RIGHT_ARM=True). + """ + + def __init__(self, model_path: str = XML_PATH) -> None: + super().__init__( + model_path=model_path, + use_left_arm=USE_LEFT_ARM, + use_right_arm=USE_RIGHT_ARM, + use_left_wrist=USE_LEFT_WRIST, + use_right_wrist=USE_RIGHT_WRIST, + ) + self.body_pose_idx = get_pos_indices(self.model, "floating_base_joint") + self.object_pose_idx = get_pos_indices(self.model, ["yellow_chair_joint"]) + self.object_vel_idx = get_vel_indices(self.model, ["yellow_chair_joint"]) + self.object_z_axis_idx = get_sensor_indices(self.model, "chair_z_axis") + self.left_palm_to_object_idx = get_sensor_indices(self.model, "trace_left_palm") + self.right_palm_to_object_idx = get_sensor_indices(self.model, "trace_right_palm") + + @property + def nu(self) -> int: + """Number of controls for this task.""" + return super().nu + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the G1 chair pushing task.""" + batch_size = states.shape[0] + config = self.config + + qpos = states[..., : self.model.nq] + qvel = states[..., self.model.nq :] + body_height = qpos[..., self.body_pose_idx[2]] + body_pos = qpos[..., self.body_pose_idx[0:3]] + object_pos = qpos[..., self.object_pose_idx[0:3]] + + object_linear_velocity = qvel[..., self.object_vel_idx[0:3]] + + object_z_axis = sensors[..., self.object_z_axis_idx] + left_palm_to_object = sensors[..., self.left_palm_to_object_idx] + right_palm_to_object = sensors[..., self.right_palm_to_object_idx] + + # Check if any state in the rollout has G1 fallen + g1_fallen_reward = -config.fall_penalty * (body_height <= config.fall_threshold).any(axis=-1) + + # Compute l2 distance from object pos. to goal (XY only for chair pushing) + goal_reward = -config.w_goal * np.linalg.norm( + object_pos[..., :2] - np.array(config.goal_position[:2])[None, None], axis=-1 + ).mean(-1) + + # Chair orientation reward - penalize if chair is not upright + chair_orientation_reward = -config.w_orientation * np.abs(np.dot(object_z_axis, Z_AXIS) - 1.0).sum(axis=-1) + + # Compute l2 distance from pelvis pos. to object pos. + pelvis_proximity_reward = config.w_pelvis_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + # Compute l2 distance from hands to object - encourage getting hands close to chair + # Use the minimum distance from either hand + left_hand_dist = np.linalg.norm(left_palm_to_object, axis=-1) + right_hand_dist = np.linalg.norm(right_palm_to_object, axis=-1) + min_hand_dist = np.minimum(left_hand_dist, right_hand_dist) + hand_proximity_reward = -config.w_hand_proximity * min_hand_dist.mean(-1) + + object_linear_velocity_penalty = -config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + # Control cost: penalize velocity commands (absolute) and arm position deviations (from default) + base_vel = controls[..., :3] + vel_cost = np.linalg.norm(base_vel, axis=-1).mean(-1) + + if self.use_left_arm or self.use_right_arm: + # Penalize arm positions as deviation from default + arm_commands = controls[..., 3:] + arm_defaults = self.default_command[3:] + arm_deviation = arm_commands - arm_defaults + arm_cost = np.linalg.norm(arm_deviation, axis=-1).mean(-1) + controls_reward = -config.w_controls * (vel_cost + arm_cost) + else: + controls_reward = -config.w_controls * vel_cost + + assert g1_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert chair_orientation_reward.shape == (batch_size,) + assert pelvis_proximity_reward.shape == (batch_size,) + assert hand_proximity_reward.shape == (batch_size,) + assert object_linear_velocity_penalty.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + return ( + g1_fallen_reward + + goal_reward + + chair_orientation_reward + + pelvis_proximity_reward + + hand_proximity_reward + + object_linear_velocity_penalty + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + # Sample chair position in front of robot + object_pos = np.array([-1, 0]) # Chair positioned in front of robot + + # Chair orientation (upright by default) + object_orientation = np.array([1, 0, 0, 0]) + + reset_object_pose = np.array([*object_pos, CHAIR_HEIGHT, *object_orientation]) + + # G1 reset pose: base position + orientation + joint positions + object pose + return np.array( + [ + -2, + 0.0, + STANDING_HEIGHT, # Standing height + 1, + 0, + 0, + 0, # Quaternion (upright orientation) + *DEFAULT_JOINT_POSITIONS, # Joint positions + *reset_object_pose, # Object pose + ] + ) + + def success( + self, model: MjModel, data: MjData, config: G1ChairPushConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if the chair is in the goal position.""" + object_pos = data.qpos[..., self.object_pose_idx[0:2]] # XY only + object_vel = data.qvel[..., self.object_vel_idx[0:3]] + goal_pos = np.array(config.goal_position[:2]) # XY only + position_check = np.linalg.norm(object_pos - goal_pos, axis=-1, ord=np.inf) < POSITION_TOLERANCE + velocity_check = np.linalg.norm(object_vel, axis=-1) < VELOCITY_TOLERANCE + return position_check and velocity_check + + def failure( + self, model: MjModel, data: MjData, config: G1ChairPushConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if G1 has fallen.""" + body_height = data.qpos[..., self.body_pose_idx[2]] + return bool(body_height <= config.fall_threshold) diff --git a/sumo/tasks/g1/g1_door.py b/sumo/tasks/g1/g1_door.py new file mode 100644 index 0000000..5beb930 --- /dev/null +++ b/sumo/tasks/g1/g1_door.py @@ -0,0 +1,225 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import mujoco +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.g1.g1_base import DEFAULT_JOINT_POSITIONS, STANDING_HEIGHT, G1Base, G1BaseConfig +from sumo.utils.indexing import get_pos_indices, get_sensor_indices + +XML_PATH = str(MODEL_PATH / "xml/g1/g1_door.xml") + +# Target door angle (in radians) - fully open is -90 degrees (-pi/2, opens away from robot) +TARGET_DOOR_ANGLE = -np.pi / 2 # -90 degrees open + +# Success condition tolerances +POSITION_TOLERANCE = 0.3 # XY position tolerance for reaching goal + +# Arm usage configuration for door manipulation +USE_LEFT_ARM = False +USE_RIGHT_ARM = True +USE_LEFT_WRIST = False +USE_RIGHT_WRIST = False + + +@dataclass +class G1DoorConfig(G1BaseConfig): + """Config for the G1 door opening task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([2.0, 0.0, STANDING_HEIGHT], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 2.0], + vis_name="goal_position", + xyz_vis_indices=[0, 1, None], + ) # Goal on other side of door + target_door_angle: float = TARGET_DOOR_ANGLE + w_goal: float = 30.0 # Weight for reaching goal position (main objective) + w_pelvis_proximity: float = ( + 10 # penalty for pelvis proximity to door (encourage using hands to keep door from pelvis) + ) + w_hand_proximity: float = 5.0 # Weight for hand proximity to door handle + w_robot_orientation: float = 50.0 # Weight for keeping robot facing forward (default orientation) + w_controls: float = 3.0 # Weight for control costs + + +class G1Door(G1Base): + config_t = G1DoorConfig + config: G1DoorConfig + + """Task getting G1 to open a hinged door. + + Uses both arms for manipulation by default (USE_LEFT_ARM=True, USE_RIGHT_ARM=True). + """ + + def __init__(self, model_path: str = XML_PATH) -> None: + super().__init__( + model_path=model_path, + use_left_arm=USE_LEFT_ARM, + use_right_arm=USE_RIGHT_ARM, + use_left_wrist=USE_LEFT_WRIST, + use_right_wrist=USE_RIGHT_WRIST, + ) + + self.body_pose_idx = get_pos_indices(self.model, "floating_base_joint") + + # Get door hinge joint indices + self.door_hinge_qpos_idx = None + self.door_hinge_qvel_idx = None + for i in range(self.model.njnt): + if mujoco.mj_id2name(self.model, mujoco.mjtObj.mjOBJ_JOINT, i) == "door_hinge": + # Find position in qpos and qvel + self.door_hinge_qpos_idx = self.model.jnt_qposadr[i] + self.door_hinge_qvel_idx = self.model.jnt_dofadr[i] + break + + if self.door_hinge_qpos_idx is None: + raise ValueError("Could not find door_hinge joint in model") + + # Sensor indices + self.door_hinge_sensor_idx = get_sensor_indices(self.model, "door_hinge_pos") + self.door_handle_idx = get_sensor_indices(self.model, "trace_door_handle") + self.left_palm_idx = get_sensor_indices(self.model, "trace_left_palm") + self.right_palm_idx = get_sensor_indices(self.model, "trace_right_palm") + + @property + def nu(self) -> int: + """Number of controls for this task.""" + return super().nu + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the G1 door opening task.""" + batch_size = states.shape[0] + config = self.config + + qpos = states[..., : self.model.nq] + qvel = states[..., self.model.nq : self.model.nq + self.model.nv] + + body_height = qpos[..., self.body_pose_idx[2]] + body_pos = qpos[..., self.body_pose_idx[0:3]] + + # Get door angle from qpos + door_angle = qpos[..., self.door_hinge_qpos_idx] + + # Get door angular velocity from qvel + door_velocity = qvel[..., self.door_hinge_qvel_idx] + + # Get sensor data + door_handle_pos = sensors[..., self.door_handle_idx] + right_palm_pos = sensors[..., self.right_palm_idx] + + # Check if any state in the rollout has G1 fallen + g1_fallen_reward = -config.fall_penalty * (body_height <= config.fall_threshold).any(axis=-1) + + # Goal position reward - MAIN OBJECTIVE: reach the other side of the door + goal_distance = np.linalg.norm(body_pos - config.goal_position[None, None], axis=-1) + goal_reward = -config.w_goal * goal_distance.mean(-1) + + # Pelvis proximity to door - encourage getting close to door + door_position = np.array([2.5, 0.005, 1.015]) # Door center position + pelvis_to_door_dist = np.linalg.norm(body_pos - door_position[None, None], axis=-1) + pelvis_proximity_reward = config.w_pelvis_proximity * pelvis_to_door_dist.mean(-1) + + # Hand proximity to door handle - encourage reaching for the handle + right_hand_to_handle = np.linalg.norm(right_palm_pos - door_handle_pos, axis=-1) + hand_proximity_reward = -config.w_hand_proximity * right_hand_to_handle.mean(-1) + + # Robot orientation reward - encourage keeping robot facing forward (default orientation) + # Get robot orientation from quaternion (w, x, y, z format in qpos) + # The robot starts facing forward (+X direction) + body_quat = qpos[..., self.body_pose_idx[3:7]] # (batch, T, 4) - quaternion [w, x, y, z] + + # Calculate forward direction (X-axis) from quaternion + # For a quaternion [w, x, y, z], the rotated forward vector (originally [1, 0, 0]) is: + # forward_x = 1 - 2(y^2 + z^2) + y_q = body_quat[..., 2] + z_q = body_quat[..., 3] + forward_x = 1 - 2 * (y_q**2 + z_q**2) + + # Reward when forward_x is positive (facing +X direction, forward) + # forward_x = 1 means perfectly facing forward (default orientation) + robot_orientation_reward = config.w_robot_orientation * forward_x.mean(-1) + + # Control cost: penalize velocity commands and arm position deviations + base_vel = controls[..., :3] + vel_cost = np.linalg.norm(base_vel, axis=-1).mean(-1) + + if self.use_left_arm or self.use_right_arm: + # Penalize arm positions as deviation from default + arm_commands = controls[..., 3:] + arm_defaults = self.default_command[3:] + arm_deviation = arm_commands - arm_defaults + arm_cost = np.linalg.norm(arm_deviation, axis=-1).mean(-1) + controls_reward = -config.w_controls * (vel_cost + arm_cost) + else: + controls_reward = -config.w_controls * vel_cost + + assert g1_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert pelvis_proximity_reward.shape == (batch_size,) + assert hand_proximity_reward.shape == (batch_size,) + assert robot_orientation_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + return ( + g1_fallen_reward + + goal_reward + + pelvis_proximity_reward + + hand_proximity_reward + + robot_orientation_reward + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and door.""" + # Door starts closed (angle = 0) + door_angle = 0.0 + + # G1 starts in front of the door, slightly offset + # Door is at x=2.5, so position G1 at x=1.0 (1.5m away) + robot_x = 0.0 + robot_y = 0.0 # Centered with door + + # G1 reset pose: base position + orientation + joint positions + door angle + return np.array( + [ + robot_x, + robot_y, + STANDING_HEIGHT, # Standing height + 1, + 0, + 0, + 0, # Quaternion (upright orientation, facing door) + *DEFAULT_JOINT_POSITIONS, # Joint positions + door_angle, # Door hinge angle + ] + ) + + def success( + self, model: MjModel, data: MjData, config: G1DoorConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if the robot has reached the target XY position.""" + body_pos = data.qpos[..., self.body_pose_idx[0:2]] # Get XY position only + goal_pos = np.array(config.goal_position[:2]) # Get XY goal only + position_check = np.linalg.norm(body_pos - goal_pos, axis=-1) < POSITION_TOLERANCE + return bool(position_check) + + def failure( + self, model: MjModel, data: MjData, config: G1DoorConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if G1 has fallen.""" + body_height = data.qpos[..., self.body_pose_idx[2]] + return bool(body_height <= config.fall_threshold) diff --git a/sumo/tasks/g1/g1_table_push.py b/sumo/tasks/g1/g1_table_push.py new file mode 100644 index 0000000..7fb7bf4 --- /dev/null +++ b/sumo/tasks/g1/g1_table_push.py @@ -0,0 +1,196 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.g1.g1_base import DEFAULT_JOINT_POSITIONS, STANDING_HEIGHT, G1Base, G1BaseConfig +from sumo.utils.indexing import get_pos_indices, get_sensor_indices, get_vel_indices + +XML_PATH = str(MODEL_PATH / "xml/g1/g1_table.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +TABLE_HEIGHT = 0.0 # Height of the table top from the ground (0.765 + 0.015) +# Annulus object position sampling +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 + +# Success condition tolerances +POSITION_TOLERANCE = 0.3 +VELOCITY_TOLERANCE = 0.1 + +# Arm usage configuration for table manipulation +USE_LEFT_ARM = True +USE_RIGHT_ARM = True +USE_LEFT_WRIST = False +USE_RIGHT_WRIST = False + + +@dataclass +class G1TablePushConfig(G1BaseConfig): + """Config for the G1 table push task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, TABLE_HEIGHT], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 1.0], + vis_name="goal_position", + xyz_vis_indices=[0, 1, None], + ) + w_goal: float = 30.0 + w_orientation: float = 15.0 + w_pelvis_proximity: float = 0.1 + w_hand_proximity: float = 10.0 + w_controls: float = 3 + + +class G1TablePush(G1Base): + config_t = G1TablePushConfig + config: G1TablePushConfig + + """Task getting G1 to push a table to a desired goal location. + + Uses both arms for manipulation by default (USE_LEFT_ARM=True, USE_RIGHT_ARM=True). + """ + + def __init__(self, model_path: str = XML_PATH) -> None: + super().__init__( + model_path=model_path, + use_left_arm=USE_LEFT_ARM, + use_right_arm=USE_RIGHT_ARM, + use_left_wrist=USE_LEFT_WRIST, + use_right_wrist=USE_RIGHT_WRIST, + ) + self.body_pose_idx = get_pos_indices(self.model, "floating_base_joint") + self.object_pose_idx = get_pos_indices(self.model, ["table_joint"]) + self.object_vel_idx = get_vel_indices(self.model, ["table_joint"]) + self.object_y_axis_idx = get_sensor_indices(self.model, "table_y_axis") + self.left_palm_to_object_idx = get_sensor_indices(self.model, "trace_left_palm") + self.right_palm_to_object_idx = get_sensor_indices(self.model, "trace_right_palm") + + @property + def nu(self) -> int: + """Number of controls for this task.""" + return super().nu + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the G1 table pushing task.""" + batch_size = states.shape[0] + config = self.config + + qpos = states[..., : self.model.nq] + body_height = qpos[..., self.body_pose_idx[2]] + body_pos = qpos[..., self.body_pose_idx[0:3]] + object_pos = qpos[..., self.object_pose_idx[0:3]] + + object_y_axis = sensors[..., self.object_y_axis_idx] + left_palm_to_object = sensors[..., self.left_palm_to_object_idx] + right_palm_to_object = sensors[..., self.right_palm_to_object_idx] + + # Check if any state in the rollout has G1 fallen + g1_fallen_reward = -config.fall_penalty * (body_height <= config.fall_threshold).any(axis=-1) + + # Compute l2 distance from object pos. to goal (XY only for table pushing) + goal_reward = -config.w_goal * np.linalg.norm( + object_pos[..., :2] - np.array(config.goal_position[:2])[None, None], axis=-1 + ).mean(-1) + + # Table orientation reward - penalize if table is not upright + table_orientation_reward = -config.w_orientation * np.abs(np.dot(object_y_axis, Z_AXIS) - 1.0).sum(axis=-1) + + # Compute l2 distance from pelvis pos. to object pos. + pelvis_proximity_reward = config.w_pelvis_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + # Compute l2 distance from hands to object - encourage getting hands close to table + # Use the minimum distance from either hand + left_hand_dist = np.linalg.norm(left_palm_to_object, axis=-1) + right_hand_dist = np.linalg.norm(right_palm_to_object, axis=-1) + min_hand_dist = np.minimum(left_hand_dist, right_hand_dist) + hand_proximity_reward = -config.w_hand_proximity * min_hand_dist.mean(-1) + + # Control cost: penalize velocity commands (absolute) and arm position deviations (from default) + base_vel = controls[..., :3] + vel_cost = np.linalg.norm(base_vel, axis=-1).mean(-1) + + if self.use_left_arm or self.use_right_arm: + # Penalize arm positions as deviation from default + arm_commands = controls[..., 3:] + arm_defaults = self.default_command[3:] + arm_deviation = arm_commands - arm_defaults + arm_cost = np.linalg.norm(arm_deviation, axis=-1).mean(-1) + controls_reward = -config.w_controls * (vel_cost + arm_cost) + else: + controls_reward = -config.w_controls * vel_cost + + assert g1_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert table_orientation_reward.shape == (batch_size,) + assert pelvis_proximity_reward.shape == (batch_size,) + assert hand_proximity_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + return ( + g1_fallen_reward + + goal_reward + + table_orientation_reward + + pelvis_proximity_reward + + hand_proximity_reward + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + # Sample table position in front of robot + object_pos = np.array([-1, 0]) # Table positioned in front of robot + + # Randomize table orientation + theta = 2 * np.pi * np.random.rand() + # object_orientation = np.array([np.cos(theta / 2), 0, 0, np.sin(theta / 2)]) + object_orientation = np.array([1, 0, 0, 0]) + + reset_object_pose = np.array([*object_pos, TABLE_HEIGHT, *object_orientation]) + + # G1 reset pose: base position + orientation + joint positions + object pose + return np.array( + [ + -2, + 0.0, + STANDING_HEIGHT, # Standing height + 1, + 0, + 0, + 0, # Quaternion (upright orientation) + *DEFAULT_JOINT_POSITIONS, # Joint positions + *reset_object_pose, # Object pose + ] + ) + + def success( + self, model: MjModel, data: MjData, config: G1TablePushConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if the table is in the goal position.""" + object_pos = data.qpos[..., self.object_pose_idx[0:2]] # XY only + object_vel = data.qvel[..., self.object_vel_idx[0:3]] + goal_pos = np.array(config.goal_position[:2]) # XY only + position_check = np.linalg.norm(object_pos - goal_pos, axis=-1, ord=np.inf) < POSITION_TOLERANCE + velocity_check = np.linalg.norm(object_vel, axis=-1) < VELOCITY_TOLERANCE + return position_check and velocity_check + + def failure( + self, model: MjModel, data: MjData, config: G1TablePushConfig, metadata: dict[str, Any] | None = None + ) -> bool: + """Check if G1 has fallen.""" + body_height = data.qpos[..., self.body_pose_idx[2]] + return bool(body_height <= config.fall_threshold) diff --git a/sumo/tasks/spot/__init__.py b/sumo/tasks/spot/__init__.py new file mode 100644 index 0000000..536dbd9 --- /dev/null +++ b/sumo/tasks/spot/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. diff --git a/sumo/tasks/spot/spot_barrier_drag.py b/sumo/tasks/spot/spot_barrier_drag.py new file mode 100644 index 0000000..df40e01 --- /dev/null +++ b/sumo/tasks/spot/spot_barrier_drag.py @@ -0,0 +1,312 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + GRIPPER_CLOSED_POS, + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_barrier.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +Y_AXIS = np.array([1.0, 0.0, 0.0]) +X_AXIS = np.array([0.0, -1.0, 0.0]) # flipped for hardware + +# annulus object position sampling +RADIUS_MIN = 1.5 +RADIUS_MAX = 3.0 +USE_LEGS = False +USE_GRIPPER = True +USE_TORSO = True + +HARDWARE_FENCE_X = (-2.0, 3.0) +HARDWARE_FENCE_Y = (-3.0, 2.5) + +DEFAULT_SPOT_POS = np.array([-1.5, 0.0]) +DEFAULT_OBJECT_POS = np.array([2.0, 0.0]) + + +@dataclass +class SpotBarrierDragConfig(SpotBaseConfig): + """Config for the spot barrier dragging task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([-0.0, 0, 0.0], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, -1.0], + maxs=[5.0, 5.0, 2.0], + steps=[0.1, 0.1, 0.1], + vis_name="barrier_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[-1.0, 1.3, 0.0], + ) + w_fence: float = 1000.0 + w_goal: float = 500.0 + w_orientation: float = 50.0 # Reward for keeping barrier aligned with global x, y, z axes + w_heading_alignment: float = 500.0 # Reward for barrier facing towards goal + object_fallen_threshold: float = 0.5 + orientation_threshold: float = 0.7 + w_torso_proximity: float = 50.0 + torso_proximity_threshold: float = 1.0 + w_object_fallen: float = 2500.0 + w_gripper_to_grasp_proximity: float = 250.0 + w_gripper_orientation: float = 100.0 + w_approach_site_proximity: float = 100.0 + w_controls: float = 2.0 + w_object_velocity: float = 10.0 + # Resistance-based grasp detection parameters + resistance_threshold: float = 0.15 + fully_closed_tolerance: float = 0.05 + w_false_grasp_penalty: float = 1000.0 + w_grasp_quality: float = 100.0 + position_tolerance: float = 0.2 + + +class SpotBarrierDrag(SpotBase[SpotBarrierDragConfig]): + """Task getting Spot to drag an upright crowd barrier to a goal location.""" + + name = "spot_barrier_drag" + config_t = SpotBarrierDragConfig + config: SpotBarrierDragConfig + + def __init__(self, config: SpotBarrierDragConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, use_gripper=True, use_torso=True, config=config) + + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("crowd_barrier_joint") + self.object_vel_idx = self.get_joint_velocity_start_index("crowd_barrier_joint") + self.object_x_axis_idx = self.get_sensor_start_index("object_x_axis") + self.object_y_axis_idx = self.get_sensor_start_index("object_y_axis") + self.object_z_axis_idx = self.get_sensor_start_index("object_z_axis") + + # Grasp point proximity sensors (left and right mid-height points for drag task) + self.gripper_to_grasp_left_idx = self.get_sensor_start_index("sensor_gripper_to_grasp_drag_left") + self.gripper_to_grasp_right_idx = self.get_sensor_start_index("sensor_gripper_to_grasp_drag_right") + + # Gripper orientation sensors + self.gripper_x_axis_idx = self.get_sensor_start_index("sensor_gripper_x_axis") + self.gripper_y_axis_idx = self.get_sensor_start_index("sensor_gripper_y_axis") + self.gripper_z_axis_idx = self.get_sensor_start_index("sensor_gripper_z_axis") + + # Gripper position sensor + self.gripper_pos_idx = self.get_sensor_start_index("trace_fngr_site") + + # Torso to approach site proximity sensors (left and right) + self.torso_to_approach_left_idx = self.get_sensor_start_index("sensor_torso_to_approach_left") + self.torso_to_approach_right_idx = self.get_sensor_start_index("sensor_torso_to_approach_right") + + # Get gripper joint position index for closure reward + self.gripper_joint_idx = self.get_joint_position_start_index("arm_f1x") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot crowd barrier dragging task.""" + batch_size = states.shape[0] + + qpos = states[..., : self.model.nq] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + object_linear_velocity = states[..., self.object_vel_idx : self.object_vel_idx + 3] + + object_z_axis = sensors[..., self.object_z_axis_idx : self.object_z_axis_idx + 3] + object_x_axis = sensors[..., self.object_x_axis_idx : self.object_x_axis_idx + 3] + object_y_axis = sensors[..., self.object_y_axis_idx : self.object_y_axis_idx + 3] + + # Grasp point proximity vectors + gripper_to_grasp_left = sensors[..., self.gripper_to_grasp_left_idx : self.gripper_to_grasp_left_idx + 3] + gripper_to_grasp_right = sensors[..., self.gripper_to_grasp_right_idx : self.gripper_to_grasp_right_idx + 3] + + # Gripper orientation axes + gripper_x_axis = sensors[..., self.gripper_x_axis_idx : self.gripper_x_axis_idx + 3] + gripper_y_axis = sensors[..., self.gripper_y_axis_idx : self.gripper_y_axis_idx + 3] + + # Gripper position + gripper_pos = sensors[..., self.gripper_pos_idx : self.gripper_pos_idx + 3] + + # Torso to approach site vectors + torso_to_approach_left = sensors[..., self.torso_to_approach_left_idx : self.torso_to_approach_left_idx + 3] + torso_to_approach_right = sensors[..., self.torso_to_approach_right_idx : self.torso_to_approach_right_idx + 3] + + # === Resistance-Based Grasp Detection === + gripper_joint_pos = qpos[..., self.gripper_joint_idx] + gripper_joint_cmd = controls[..., 9] # ARM_CMD_INDS[6] maps to controls index 9 + + # Position error: actual - commanded + position_error = gripper_joint_pos - gripper_joint_cmd + + # Grasp detection logic + has_resistance = position_error > self.config.resistance_threshold + is_fully_closed = np.abs(gripper_joint_pos - GRIPPER_CLOSED_POS) < self.config.fully_closed_tolerance + is_not_empty = ~is_fully_closed + is_grasping = has_resistance & is_not_empty + is_false_grasp = is_fully_closed & (gripper_joint_cmd < -0.5) + + # Fence reward + fence_violated_x = (body_pos[..., 0] < HARDWARE_FENCE_X[0]) | (body_pos[..., 0] > HARDWARE_FENCE_X[1]) + fence_violated_y = (body_pos[..., 1] < HARDWARE_FENCE_Y[0]) | (body_pos[..., 1] > HARDWARE_FENCE_Y[1]) + spot_fence_reward = -self.config.w_fence * (fence_violated_x | fence_violated_y).any(axis=-1) + + # Check if spot has fallen + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Check if object has fallen + object_fallen_reward = -self.config.w_object_fallen * ( + object_pos[..., 2] <= self.config.object_fallen_threshold + ).any(axis=-1) + + # Goal reward - distance to goal position + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + # Orientation reward - keep barrier aligned with global x, y, z axes + x_alignment = np.sum(object_x_axis * X_AXIS, axis=-1) + y_alignment = np.sum(object_y_axis * Y_AXIS, axis=-1) + z_alignment = np.sum(object_z_axis * Z_AXIS, axis=-1) + orientation_error = (1.0 - x_alignment) + (1.0 - y_alignment) + (1.0 - z_alignment) + object_orientation_reward = -self.config.w_orientation * orientation_error.mean(axis=-1) + + # Heading alignment reward - keep barrier's x-axis pointing towards goal + barrier_to_goal = np.array(self.config.goal_position)[None, None, :2] - object_pos[..., :2] + barrier_to_goal_norm = barrier_to_goal / (np.linalg.norm(barrier_to_goal, axis=-1, keepdims=True) + 1e-8) + + object_x_horizontal = object_x_axis[..., :2] + object_x_horizontal_norm = object_x_horizontal / ( + np.linalg.norm(object_x_horizontal, axis=-1, keepdims=True) + 1e-8 + ) + + heading_alignment = np.sum(object_x_horizontal_norm * barrier_to_goal_norm, axis=-1) + heading_alignment_abs = np.abs(heading_alignment) + heading_error = 1.0 - heading_alignment_abs + heading_alignment_reward = -self.config.w_heading_alignment * heading_error.mean(axis=-1) + + # Approach site proximity reward + distance_to_left_approach = np.linalg.norm(torso_to_approach_left, axis=-1) + distance_to_right_approach = np.linalg.norm(torso_to_approach_right, axis=-1) + approach_distances = np.stack([distance_to_left_approach, distance_to_right_approach], axis=-1) + min_approach_distance = np.min(approach_distances, axis=-1) + approach_site_proximity_reward = -self.config.w_approach_site_proximity * min_approach_distance.mean(-1) + + # Torso proximity reward + torso_proximity_reward = self.config.w_torso_proximity * np.minimum( + self.config.torso_proximity_threshold, np.linalg.norm(body_pos - object_pos, axis=-1) + ).mean(-1) + + # Gripper to grasp point proximity + distance_to_left = np.linalg.norm(gripper_to_grasp_left, axis=-1) + distance_to_right = np.linalg.norm(gripper_to_grasp_right, axis=-1) + grasp_distances = np.stack([distance_to_left, distance_to_right], axis=-1) + min_grasp_distance = np.min(grasp_distances, axis=-1) + gripper_to_grasp_proximity_reward = -self.config.w_gripper_to_grasp_proximity * min_grasp_distance.mean(-1) + + # Gripper orientation reward - adaptive based on position + object_to_gripper = gripper_pos - object_pos + + gripper_x_axis_norm = gripper_x_axis / (np.linalg.norm(gripper_x_axis, axis=-1, keepdims=True) + 1e-8) + object_y_axis_norm = object_y_axis / (np.linalg.norm(object_y_axis, axis=-1, keepdims=True) + 1e-8) + + # Determine if gripper is in front (+Y) or behind (-Y) the object + projection_on_y = np.sum(object_to_gripper * object_y_axis_norm, axis=-1, keepdims=True) + + # Gripper should point toward the object + target_y_axis = np.where(projection_on_y > 0, -object_y_axis_norm, object_y_axis_norm) + + x_dot_product = np.sum(gripper_x_axis_norm * target_y_axis, axis=-1) + x_alignment_error = 1.0 - x_dot_product + + # Gripper Y-axis aligned with object Z-axis (upward) + gripper_y_axis_norm = gripper_y_axis / (np.linalg.norm(gripper_y_axis, axis=-1, keepdims=True) + 1e-8) + object_z_axis_norm = object_z_axis / (np.linalg.norm(object_z_axis, axis=-1, keepdims=True) + 1e-8) + y_dot_product = np.sum(gripper_y_axis_norm * object_z_axis_norm, axis=-1) + y_alignment_error = 1.0 - y_dot_product + + total_orientation_error = x_alignment_error + y_alignment_error + gripper_orientation_reward = -self.config.w_gripper_orientation * total_orientation_error.mean(axis=-1) + + # Object velocity penalty + object_linear_velocity_reward = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + + # Control penalty + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + # Grasp-based rewards + false_grasp_penalty = -self.config.w_false_grasp_penalty * is_false_grasp.any(axis=-1) + grasp_quality = np.clip(position_error / 0.5, 0, 1) + grasp_quality_reward = self.config.w_grasp_quality * (is_grasping * grasp_quality).mean(axis=-1) + + assert spot_fence_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert object_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert object_orientation_reward.shape == (batch_size,) + assert heading_alignment_reward.shape == (batch_size,) + assert approach_site_proximity_reward.shape == (batch_size,) + assert torso_proximity_reward.shape == (batch_size,) + assert gripper_to_grasp_proximity_reward.shape == (batch_size,) + assert gripper_orientation_reward.shape == (batch_size,) + assert object_linear_velocity_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + assert false_grasp_penalty.shape == (batch_size,) + assert grasp_quality_reward.shape == (batch_size,) + + return ( + spot_fence_reward + + spot_fallen_reward + + object_fallen_reward + + goal_reward + + object_orientation_reward + + heading_alignment_reward + + gripper_to_grasp_proximity_reward + + gripper_orientation_reward + + false_grasp_penalty + + grasp_quality_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object - barrier starts upright.""" + # Barrier starts upright (no rotation) + reset_object_pose = np.array([*DEFAULT_OBJECT_POS, 0.035, 1, 0, 0, 0]) + + return np.array( + [ + *DEFAULT_SPOT_POS, + STANDING_HEIGHT, + 1, + 0, + 0, + 0, + *LEGS_STANDING_POS, + *self.reset_arm_pos, + *reset_object_pose, + ] + ) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the barrier has been successfully dragged to the goal position.""" + object_pos = data.qpos[self.object_pose_idx : self.object_pose_idx + 3] + goal_position = np.array(self.config.goal_position) + + # Check position tolerance + distance_to_goal = np.linalg.norm(object_pos - goal_position) + position_success = distance_to_goal <= self.config.position_tolerance + + return bool(position_success) diff --git a/sumo/tasks/spot/spot_barrier_upright.py b/sumo/tasks/spot/spot_barrier_upright.py new file mode 100644 index 0000000..77301b5 --- /dev/null +++ b/sumo/tasks/spot/spot_barrier_upright.py @@ -0,0 +1,294 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + GRIPPER_CLOSED_POS, + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_barrier.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +# annulus object position sampling +RADIUS_MIN = 1.5 +RADIUS_MAX = 3.0 +USE_LEGS = False +USE_GRIPPER = True +USE_TORSO = True + +HARDWARE_FENCE_X = (-2.0, 3.0) +HARDWARE_FENCE_Y = (-3.0, 2.5) + +DEFAULT_SPOT_POS = np.array([0.0, 0.0]) +DEFAULT_OBJECT_POS = np.array([2.0, 0.0]) + + +@dataclass +class SpotBarrierUprightConfig(SpotBaseConfig): + """Config for the spot barrier uprighting task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([-1.0, 1.3, 0.0], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, -1.0], + maxs=[5.0, 5.0, 2.0], + steps=[0.1, 0.1, 0.1], + vis_name="barrier_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[-1.0, 1.3, 0.0], + ) + w_goal: float = 0.0 # Override base class default + w_fence: float = 1000.0 + w_orientation: float = 100.0 + orientation_sparsity: float = 3.0 + orientation_threshold: float = 0.7 + w_gripper_to_grasp_proximity: float = 500.0 # Reward for gripper proximity to grasp points + w_gripper_orientation: float = 100.0 + w_controls: float = 2.0 + w_object_velocity: float = 10.0 + w_approach_site_proximity: float = 10.0 # Reward for torso proximity to approach sites + # Resistance-based grasp detection parameters + resistance_threshold: float = 0.15 # Position error indicating resistance (radians) + fully_closed_tolerance: float = 0.05 # Tolerance for "fully closed" detection + w_false_grasp_penalty: float = 1000.0 # Penalty for false grasp (closed but not grasping) + w_grasp_quality: float = 100.0 # Reward for high-quality grasp + position_tolerance: float = 0.2 + orientation_tolerance: float = 0.1 + + +class SpotBarrierUpright(SpotBase[SpotBarrierUprightConfig]): + """Task getting Spot to upright a fallen crowd barrier.""" + + name = "spot_barrier_upright" + config_t = SpotBarrierUprightConfig + config: SpotBarrierUprightConfig + + def __init__(self, config: SpotBarrierUprightConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, use_gripper=True, use_torso=True, config=config) + + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("crowd_barrier_joint") + self.object_vel_idx = self.get_joint_velocity_start_index("crowd_barrier_joint") + self.object_x_axis_idx = self.get_sensor_start_index("object_x_axis") + self.object_y_axis_idx = self.get_sensor_start_index("object_y_axis") + self.object_z_axis_idx = self.get_sensor_start_index("object_z_axis") + + # Grasp point proximity sensors (left and right mid-height points) + self.gripper_to_grasp_left_idx = self.get_sensor_start_index("sensor_gripper_to_grasp_left") + self.gripper_to_grasp_right_idx = self.get_sensor_start_index("sensor_gripper_to_grasp_right") + + # Gripper orientation sensors + self.gripper_x_axis_idx = self.get_sensor_start_index("sensor_gripper_x_axis") + self.gripper_y_axis_idx = self.get_sensor_start_index("sensor_gripper_y_axis") + self.gripper_z_axis_idx = self.get_sensor_start_index("sensor_gripper_z_axis") + + # Torso to approach site proximity sensors (left and right) + self.torso_to_approach_left_idx = self.get_sensor_start_index("sensor_torso_to_approach_left") + self.torso_to_approach_right_idx = self.get_sensor_start_index("sensor_torso_to_approach_right") + + # Get gripper joint position index for closure reward + self.gripper_joint_idx = self.get_joint_position_start_index("arm_f1x") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot crowd barrier uprighting task.""" + batch_size = states.shape[0] + + qpos = states[..., : self.model.nq] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + object_linear_velocity = states[..., self.object_vel_idx : self.object_vel_idx + 3] + + object_z_axis = sensors[..., self.object_z_axis_idx : self.object_z_axis_idx + 3] + object_x_axis = sensors[..., self.object_x_axis_idx : self.object_x_axis_idx + 3] + object_y_axis = sensors[..., self.object_y_axis_idx : self.object_y_axis_idx + 3] + + # Grasp point proximity vectors + gripper_to_grasp_left = sensors[..., self.gripper_to_grasp_left_idx : self.gripper_to_grasp_left_idx + 3] + gripper_to_grasp_right = sensors[..., self.gripper_to_grasp_right_idx : self.gripper_to_grasp_right_idx + 3] + + # Gripper orientation axes + gripper_x_axis = sensors[..., self.gripper_x_axis_idx : self.gripper_x_axis_idx + 3] + gripper_y_axis = sensors[..., self.gripper_y_axis_idx : self.gripper_y_axis_idx + 3] + + # Approach site proximity vectors + torso_to_approach_left = sensors[..., self.torso_to_approach_left_idx : self.torso_to_approach_left_idx + 3] + torso_to_approach_right = sensors[..., self.torso_to_approach_right_idx : self.torso_to_approach_right_idx + 3] + + fence_violated_x = (body_pos[..., 0] < HARDWARE_FENCE_X[0]) | (body_pos[..., 0] > HARDWARE_FENCE_X[1]) + fence_violated_y = (body_pos[..., 1] < HARDWARE_FENCE_Y[0]) | (body_pos[..., 1] > HARDWARE_FENCE_Y[1]) + spot_fence_reward = -self.config.w_fence * (fence_violated_x | fence_violated_y).any(axis=-1) + + # Check if any state in the rollout has spot fallen + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Compute l2 distance from object pos. to goal. + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + # Reward for uprighting the barrier (z-axis pointing up) + orientation_alignement = np.minimum( + np.dot(object_z_axis, Z_AXIS) - 1, self.config.orientation_threshold + ) # ranging from -2 to 0 + object_orientation_reward = +self.config.w_orientation * np.exp( + self.config.orientation_sparsity * orientation_alignement + ).sum(axis=-1) # ranging from 0 to w_orientation + + # Compute minimum distance from gripper to grasp points (left or right) + distance_to_left = np.linalg.norm(gripper_to_grasp_left, axis=-1) + distance_to_right = np.linalg.norm(gripper_to_grasp_right, axis=-1) + + # Stack distances and find minimum across grasp points + grasp_distances = np.stack([distance_to_left, distance_to_right], axis=-1) + min_grasp_distance = np.min(grasp_distances, axis=-1) + + # Reward for being close to the nearest grasp point + gripper_to_grasp_proximity_reward = -self.config.w_gripper_to_grasp_proximity * min_grasp_distance.mean(-1) + + # Gripper orientation reward + # Barrier frame: X-axis = long dimension (2.191m), Y-axis = thin depth, Z-axis = height + # Gripper should approach along barrier's long axis: gripper X-axis aligns with barrier X-axis + # and gripper Y-axis aligns with barrier Z-axis (for upright grasping) + gripper_x_axis_norm = gripper_x_axis / (np.linalg.norm(gripper_x_axis, axis=-1, keepdims=True) + 1e-8) + object_x_axis_norm = object_x_axis / (np.linalg.norm(object_x_axis, axis=-1, keepdims=True) + 1e-8) + x_dot_product = np.abs(np.sum(gripper_x_axis_norm * object_x_axis_norm, axis=-1)) + x_alignment_error = 1.0 - x_dot_product + + gripper_y_axis_norm = gripper_y_axis / (np.linalg.norm(gripper_y_axis, axis=-1, keepdims=True) + 1e-8) + object_z_axis_norm = object_z_axis / (np.linalg.norm(object_z_axis, axis=-1, keepdims=True) + 1e-8) + y_dot_product = np.abs(np.sum(gripper_y_axis_norm * object_z_axis_norm, axis=-1)) + y_alignment_error = 1.0 - y_dot_product + + total_orientation_error = x_alignment_error + y_alignment_error + gripper_orientation_reward = -self.config.w_gripper_orientation * total_orientation_error.mean(axis=-1) + + # Approach site proximity reward - encourage torso to get close to either left or right approach site + distance_to_left_approach = np.linalg.norm(torso_to_approach_left, axis=-1) + distance_to_right_approach = np.linalg.norm(torso_to_approach_right, axis=-1) + approach_distances = np.stack([distance_to_left_approach, distance_to_right_approach], axis=-1) + min_approach_distance = np.min(approach_distances, axis=-1) + approach_site_proximity_reward = -self.config.w_approach_site_proximity * min_approach_distance.mean(-1) + + # === Resistance-Based Grasp Detection === + # Key insight: If commanded to close but actual position stalls → object is blocking → GRASPING + # If gripper reaches fully closed (0.0) → no resistance → NOT GRASPING + + gripper_joint_pos = qpos[..., self.gripper_joint_idx] # Actual position (0.0=closed, -1.54=open) + gripper_joint_cmd = controls[..., 9] # ARM_CMD_INDS[6] maps to controls index 9 + + # Position error: actual - commanded + # Positive error means gripper is MORE OPEN than commanded (being blocked by object) + position_error = gripper_joint_pos - gripper_joint_cmd + # print(f"position_error shape: {position_error.shape}") + # Grasp detection logic: + # 1. Has resistance: gripper can't reach commanded position + has_resistance = position_error > self.config.resistance_threshold + + # 2. NOT fully closed: if gripper reached 0.0, nothing is blocking it + is_fully_closed = np.abs(gripper_joint_pos - GRIPPER_CLOSED_POS) < self.config.fully_closed_tolerance + is_not_empty = ~is_fully_closed + + # 3. Combined: grasping = has resistance AND not empty + is_grasping = has_resistance & is_not_empty + + # 4. False grasp: gripper is closed but NOT grasping (should be penalized) + # This happens when gripper closes in empty space + is_false_grasp = is_fully_closed & (gripper_joint_cmd < -0.5) # Commanded to be open but ended up closed + + # Compute squared l2 norm of the object velocity. + object_linear_velocity_reward = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + + # Compute a velocity penalty to prefer small velocity commands. + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + # === Grasp-based rewards === + # Penalize false grasps (gripper closed but nothing in grasp) + false_grasp_penalty = -self.config.w_false_grasp_penalty * is_false_grasp.any(axis=-1) + + # Reward for successful grasp (based on resistance strength) + # Higher position_error = stronger resistance = better grasp + grasp_quality = np.clip(position_error / 0.5, 0, 1) # Normalize to [0, 1] + # print(f"gras_quality shape: {grasp_quality.shape}") + # print(f"is_grasping shape: {is_grasping.shape}") + grasp_quality_reward = self.config.w_grasp_quality * (is_grasping * grasp_quality).mean(axis=-1) + + assert spot_fence_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert object_orientation_reward.shape == (batch_size,) + assert gripper_to_grasp_proximity_reward.shape == (batch_size,) + assert approach_site_proximity_reward.shape == (batch_size,) + assert object_linear_velocity_reward.shape == (batch_size,) + assert gripper_orientation_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + assert false_grasp_penalty.shape == (batch_size,) + assert grasp_quality_reward.shape == (batch_size,) + + return ( + # spot_fence_reward + +spot_fallen_reward + + goal_reward + + object_orientation_reward + + gripper_to_grasp_proximity_reward + + gripper_orientation_reward + + approach_site_proximity_reward + + object_linear_velocity_reward + + controls_reward + + false_grasp_penalty # Penalize closing gripper in empty space + + grasp_quality_reward # Reward successful grasp with resistance + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + theta = 2 * np.pi * np.random.rand() + # object_pos = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + object_pos = np.array([2.0, 0]) + # Fallen orientation for uprighting task (rotated 90 degrees around y-axis) + reset_object_pose = np.array([*object_pos, 0.3, np.cos(np.pi / 4), -np.sin(np.pi / 4), 0, 0]) + + return np.array( + [ + # *np.random.randn(2), + *np.array([0, 0]), + STANDING_HEIGHT, + 1, + 0, + 0, + 0, + *LEGS_STANDING_POS, + *self.reset_arm_pos, + *reset_object_pose, + ] + ) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the crowd barrier is upright, regardless of position.""" + # Get object z-axis sensor data for orientation check + object_z_axis = data.sensordata[self.object_z_axis_idx : self.object_z_axis_idx + 3] + + # Check orientation tolerance (object should be upright, z-axis aligned with world z-axis) + orientation_alignment = np.dot(object_z_axis, Z_AXIS) + orientation_success = orientation_alignment >= (1.0 - self.config.orientation_tolerance) + + return bool(orientation_success) diff --git a/sumo/tasks/spot/spot_base.py b/sumo/tasks/spot/spot_base.py new file mode 100644 index 0000000..bc773e9 --- /dev/null +++ b/sumo/tasks/spot/spot_base.py @@ -0,0 +1,170 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from __future__ import annotations + +import hashlib +import re +import tempfile +from pathlib import Path +from typing import Any, Generic, TypeVar + +from judo import MODEL_PATH as JUDO_MODEL_PATH +from judo.tasks.spot.spot_base import SpotBase as _JudoSpotBase +from judo.tasks.spot.spot_base import SpotBaseConfig + +from sumo import MODEL_PATH + +XML_PATH = str(JUDO_MODEL_PATH / "xml" / "spot_primitive" / "robot.xml") + +ConfigT = TypeVar("ConfigT", bound=SpotBaseConfig) + +_INCLUDE_RE = re.compile(r'( Path: + try: + from robot_descriptions import spot_mj_description # noqa: PLC0415 + except Exception as exc: # pragma: no cover - exercised via failure-path test + raise RuntimeError( + "Spot tasks require `robot_descriptions` to resolve Spot robot assets. " + "Run from the root pixi environment (for example `pixi run sumo`) " + "or install `robot_descriptions` in the active environment." + ) from exc + + return Path(spot_mj_description.PACKAGE_PATH) + + +def _resolve_public_object_asset(relpath: str) -> Path | None: + if "objects/" not in relpath: + return None + + suffix = relpath[relpath.rindex("objects/") :] + candidates = [suffix] + if suffix.startswith("objects/tire/meshes/"): + compat_suffix = suffix.replace("objects/tire/meshes/", "objects/tire/", 1) + if compat_suffix.endswith("/visual/tire.obj"): + compat_suffix = compat_suffix[: -len("/visual/tire.obj")] + "/visual/tire_rubber.obj" + candidates.insert(0, compat_suffix) + + for root in (JUDO_MODEL_PATH / "meshes", MODEL_PATH / "meshes"): + for candidate_suffix in candidates: + candidate = root / candidate_suffix + if candidate.exists(): + return candidate + return None + + +class SpotAssetMixin: + """Align Spot robot assets with judo while preserving local object compatibility.""" + + spec: Any # MjSpec, provided by SpotBase via MRO + + def _process_spec(self) -> None: + menagerie_dir = _get_spot_menagerie_dir() + menagerie_assets = menagerie_dir / "assets" + + for mesh in self.spec.meshes: + if "spot/meshes/" in mesh.file: + mesh.file = str(menagerie_assets / Path(mesh.file).name) + continue + + asset_path = _resolve_public_object_asset(mesh.file) + if asset_path is not None: + mesh.file = str(asset_path) + + for texture in self.spec.textures: + if "spot/textures/" in texture.file: + texture.file = str(menagerie_dir / "spot.png") + + +class SpotBase(SpotAssetMixin, _JudoSpotBase, Generic[ConfigT]): + """Sumo SpotBase wrapper that composes local task XML with public Spot definitions.""" + + @staticmethod + def _is_relative_to(path: Path, parent: Path) -> bool: + try: + path.relative_to(parent) + return True + except ValueError: + return False + + @classmethod + def _resolve_include_path(cls, include_path: str, source_dir: Path) -> Path: + include = Path(include_path) + basename = include.name + + if basename in _SPOT_PRIMITIVE_FILES: + return _SPOT_PRIMITIVE_FILES[basename] + if basename in _SUMO_SPOT_EXTENSION_FILES: + return _SUMO_SPOT_EXTENSION_FILES[basename] + if basename in _PUBLIC_OBJECT_COMPAT: + return _PUBLIC_OBJECT_COMPAT[basename] + + candidate = include if include.is_absolute() else (source_dir / include).resolve() + if candidate.exists(): + return candidate + + flat_object_candidate = MODEL_PATH / "xml" / "objects" / basename + if flat_object_candidate.exists(): + return flat_object_candidate + + nested_object_candidate = MODEL_PATH / "xml" / "objects" / include.stem / basename + if nested_object_candidate.exists(): + return nested_object_candidate + + raise FileNotFoundError(f"Unable to resolve include '{include_path}' from '{source_dir}'.") + + @classmethod + def _materialize_model_path(cls, model_path: str | Path) -> Path: + path = Path(model_path).expanduser().resolve() + if cls._is_relative_to(path, JUDO_MODEL_PATH): + return path + + source = re.sub(r"", "", path.read_text(), flags=re.S) + + def replace_include(match: re.Match[str]) -> str: + resolved = cls._resolve_include_path(match.group(2), path.parent) + return f"{match.group(1)}{resolved.as_posix()}{match.group(3)}" + + rewritten = _INCLUDE_RE.sub(replace_include, source) + cache_dir = Path(tempfile.gettempdir()) / "sumo-spot-models" + cache_dir.mkdir(parents=True, exist_ok=True) + digest = hashlib.sha256(rewritten.encode("utf-8")).hexdigest()[:12] + materialized_path = cache_dir / f"{path.stem}-{digest}.xml" + if not materialized_path.exists(): + materialized_path.write_text(rewritten) + return materialized_path + + def __init__( + self, + model_path: str = XML_PATH, + use_arm: bool = True, + use_gripper: bool = False, + use_legs: bool = False, + use_torso: bool = False, + config: SpotBaseConfig | None = None, + ) -> None: + super().__init__( + model_path=str(self._materialize_model_path(model_path)), + use_arm=use_arm, + use_gripper=use_gripper, + use_legs=use_legs, + use_torso=use_torso, + config=config, + ) diff --git a/sumo/tasks/spot/spot_box_push.py b/sumo/tasks/spot/spot_box_push.py new file mode 100644 index 0000000..c1be174 --- /dev/null +++ b/sumo/tasks/spot/spot_box_push.py @@ -0,0 +1,13 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.tasks.spot.spot_box_push import SpotBoxPush as _JudoSpotBoxPush +from judo.tasks.spot.spot_box_push import SpotBoxPushConfig + +from sumo.tasks.spot.spot_base import SpotAssetMixin + + +class SpotBoxPush(SpotAssetMixin, _JudoSpotBoxPush): + """Public judo-rai SpotBoxPush with Sumo asset resolution.""" + + +__all__ = ["SpotBoxPush", "SpotBoxPushConfig"] diff --git a/sumo/tasks/spot/spot_chair_push.py b/sumo/tasks/spot/spot_chair_push.py new file mode 100644 index 0000000..de8d2f6 --- /dev/null +++ b/sumo/tasks/spot/spot_chair_push.py @@ -0,0 +1,162 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""SpotChairPush task - push a yellow chair to a goal. + +Adapted from judo/tasks/spot/spot_yellow_chair_push.py. +""" + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.tasks.spot.spot_constants import Z_AXIS +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import LEGS_STANDING_POS, STANDING_HEIGHT + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_yellow_chair.xml") + +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 +HARDWARE_FENCE_X = (-2.0, 3.0) +HARDWARE_FENCE_Y = (-3.0, 2.5) + + +@dataclass +class SpotChairPushConfig(SpotBaseConfig): + """Configuration for the SpotChairPush task.""" + + w_fence: float = 1000.0 + w_goal: float = 60.0 + w_orientation: float = 50.0 + orientation_sparsity: float = 5.0 + w_torso_proximity: float = 50.0 + torso_proximity_threshold: float = 1.0 + w_gripper_proximity: float = 8.0 + orientation_threshold: float = 0.7 + w_controls: float = 2.0 + w_object_velocity: float = 64.0 + fall_penalty: float = 2500.0 + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, 0.0]), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 3.0], + vis_name="goal_position", + xyz_vis_indices=[0, 1, None], + ) + + # Success criteria (sumo-specific) + orientation_tolerance: float = 0.1 + + +class SpotChairPush(SpotBase[SpotChairPushConfig]): + """Task getting Spot to push a yellow chair to a desired goal location.""" + + name: str = "spot_chair_push" + config_t: type[SpotChairPushConfig] = SpotChairPushConfig # type: ignore[assignment] + config: SpotChairPushConfig + + def __init__( + self, + config: SpotChairPushConfig | None = None, + ) -> None: + """Initialize the SpotChairPush task.""" + super().__init__(model_path=XML_PATH, use_arm=True, config=config) + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("yellow_chair_joint") + self.gripper_pos_idx = self.get_sensor_start_index("trace_fngr_site") + self.object_z_axis_idx = self.get_sensor_start_index("object_z_axis") + self.object_vel_idx = self.model.jnt_dofadr[self.model.joint("yellow_chair_joint").id] + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the chair push task.""" + batch_size = states.shape[0] + qpos = states[..., : self.model.nq] + qvel = states[..., self.model.nq :] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + object_linear_velocity = qvel[..., self.object_vel_idx : self.object_vel_idx + 3] + object_z_axis = sensors[..., self.object_z_axis_idx : self.object_z_axis_idx + 3] + gripper_pos = sensors[..., self.gripper_pos_idx : self.gripper_pos_idx + 3] + + # Fence penalty + fence_violated_x = (body_pos[..., 0] < HARDWARE_FENCE_X[0]) | (body_pos[..., 0] > HARDWARE_FENCE_X[1]) + fence_violated_y = (body_pos[..., 1] < HARDWARE_FENCE_Y[0]) | (body_pos[..., 1] > HARDWARE_FENCE_Y[1]) + spot_fence_reward = -self.config.w_fence * (fence_violated_x | fence_violated_y).any(axis=-1) + + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - self.config.goal_position[None, None], axis=-1 + ).mean(-1) + + orientation_alignment = np.minimum(np.dot(object_z_axis, Z_AXIS) - 1, self.config.orientation_threshold) + object_orientation_reward = +self.config.w_orientation * np.exp( + self.config.orientation_sparsity * orientation_alignment + ).sum(axis=-1) + + torso_proximity_reward = self.config.w_torso_proximity * np.minimum( + self.config.torso_proximity_threshold, np.linalg.norm(body_pos - object_pos, axis=-1) + ).mean(-1) + + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + gripper_pos - object_pos, axis=-1 + ).mean(-1) + + object_linear_velocity_reward = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + assert spot_fence_reward.shape == (batch_size,) + return ( + spot_fence_reward + + spot_fallen_reward + + goal_reward + + object_orientation_reward + + torso_proximity_reward + + gripper_proximity_reward + + object_linear_velocity_reward + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose for the chair push task.""" + radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + theta = 2 * np.pi * np.random.rand() + object_xy = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + np.random.randn(2) + reset_object_pose = np.array([*object_xy, 0, 1, 0, 0, 0]) + return np.array( + [ + *np.random.randn(2), + STANDING_HEIGHT, + 1, + 0, + 0, + 0, + *LEGS_STANDING_POS, + *self.reset_arm_pos, + *reset_object_pose, + ] + ) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the yellow chair is upright, regardless of position.""" + object_z_axis = data.sensordata[self.object_z_axis_idx : self.object_z_axis_idx + 3] + orientation_alignment = np.dot(object_z_axis, Z_AXIS) + orientation_success = orientation_alignment >= (1.0 - self.config.orientation_tolerance) + return bool(orientation_success) diff --git a/sumo/tasks/spot/spot_chair_ramp.py b/sumo/tasks/spot/spot_chair_ramp.py new file mode 100644 index 0000000..cd0f9b5 --- /dev/null +++ b/sumo/tasks/spot/spot_chair_ramp.py @@ -0,0 +1,174 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""SpotChairRamp task - push a chair up a ramp. + +Adapted from judo/tasks/spot/spot_yellow_chair_ramp_push.py. +""" + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.tasks.spot.spot_constants import Z_AXIS +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import LEGS_STANDING_POS, STANDING_HEIGHT + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_yellow_chair_ramp.xml") + +# Minimum distance between robot and chair centers to avoid collision at reset +MIN_ROBOT_OBJECT_DISTANCE = 1.2 + + +@dataclass +class SpotChairRampConfig(SpotBaseConfig): + """Configuration for the SpotChairRamp task.""" + + w_goal: float = 100.0 + w_orientation: float = 100.0 + w_torso_proximity: float = 0.25 + w_gripper_proximity: float = 4.0 + orientation_threshold: float = 0.4 + w_controls: float = 2.0 + w_object_velocity: float = 128.0 + fall_penalty: float = 10000.0 + w_object_off_ramp: float = 1000.0 + w_object_centered: float = 15.0 + w_spot_off_ramp: float = 1000.0 + goal_position: np.ndarray = np_1d_field( + np.array([2.0, 4.5, 0.5]), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 3.0], + vis_name="goal_position", + xyz_vis_indices=[0, 1, None], + ) + + # Success criteria (sumo-specific) + position_tolerance: float = 0.005 + + +class SpotChairRamp(SpotBase[SpotChairRampConfig]): + """Task getting Spot to push a yellow chair up a ramp.""" + + name: str = "spot_chair_ramp" + config_t: type[SpotChairRampConfig] = SpotChairRampConfig # type: ignore[assignment] + config: SpotChairRampConfig + + def __init__( + self, + config: SpotChairRampConfig | None = None, + ) -> None: + """Initialize the SpotChairRamp task.""" + super().__init__(model_path=XML_PATH, use_arm=True, config=config) + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("yellow_chair_joint") + self.gripper_pos_idx = self.get_sensor_start_index("trace_fngr_site") + self.object_y_axis_idx = self.get_sensor_start_index("object_y_axis") + self.object_vel_idx = self.model.jnt_dofadr[self.model.joint("yellow_chair_joint").id] + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the chair ramp push task.""" + batch_size = states.shape[0] + qpos = states[..., : self.model.nq] + qvel = states[..., self.model.nq :] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + object_linear_velocity = qvel[..., self.object_vel_idx : self.object_vel_idx + 3] + object_y_axis = sensors[..., self.object_y_axis_idx : self.object_y_axis_idx + 3] + gripper_pos = sensors[..., self.gripper_pos_idx : self.gripper_pos_idx + 3] + + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - self.config.goal_position[None, None], axis=-1 + ).mean(-1) + + object_orientation_reward = -self.config.w_orientation * ( + np.abs(np.dot(object_y_axis, Z_AXIS)) > self.config.orientation_threshold + ).sum(axis=-1) + + torso_proximity_reward = self.config.w_torso_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + gripper_pos - object_pos, axis=-1 + ).mean(-1) + + object_linear_velocity_reward = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + + # Off-ramp penalties + object_off_ramp_x = np.logical_or(object_pos[..., 0] <= 1.0, object_pos[..., 0] >= 3.0) + object_off_ramp_y = object_pos[..., 1] >= 5.25 + object_off_ramp = np.logical_or(object_off_ramp_x, object_off_ramp_y) + object_off_ramp_penalty = -self.config.w_object_off_ramp * object_off_ramp.mean(-1) + + object_centered = -self.config.w_object_centered * np.abs(object_pos[..., 0] - 2.0).mean(-1) + + spot_off_ramp = body_pos[..., 1] >= 4.5 + spot_off_ramp_penalty = -self.config.w_spot_off_ramp * spot_off_ramp.mean(-1) + + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + assert spot_fallen_reward.shape == (batch_size,) + return ( + spot_fallen_reward + + goal_reward + + object_orientation_reward + + torso_proximity_reward + + gripper_proximity_reward + + object_linear_velocity_reward + + controls_reward + + object_off_ramp_penalty + + object_centered + + spot_off_ramp_penalty + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose for the chair ramp push task. + + Places the chair near the ramp base and the robot behind it, + ensuring they don't overlap. + """ + # Place chair at the base of the ramp with small randomization + object_xy = np.array([2.0, 0.5]) + np.random.uniform(-0.3, 0.3, 2) + reset_object_pose = np.array([*object_xy, 0, 1, 0, 0, 0]) + + # Place robot behind the chair (negative y), ensuring no overlap + robot_xy = object_xy[:2] + np.array([0.0, -MIN_ROBOT_OBJECT_DISTANCE]) + robot_xy += np.random.uniform(-2.0, 2.0, 2) + # Ensure minimum separation + while np.linalg.norm(robot_xy - object_xy[:2]) < MIN_ROBOT_OBJECT_DISTANCE: + robot_xy = object_xy[:2] + np.array([0.0, -MIN_ROBOT_OBJECT_DISTANCE]) + robot_xy += np.random.uniform(-0.2, 0.2, 2) + + return np.array( + [*robot_xy, STANDING_HEIGHT, 1, 0, 0, 0, *LEGS_STANDING_POS, *self.reset_arm_pos, *reset_object_pose] + ) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the yellow chair has reached the top platform area of the ramp.""" + object_pos = data.qpos[self.object_pose_idx : self.object_pose_idx + 3] + + platform_x_min, platform_x_max = 0.66, 3.09 + platform_y_min, platform_y_max = 4.192, 5.412 + platform_z_min = 0.20 + + x_in_bounds = platform_x_min <= object_pos[0] <= platform_x_max + y_in_bounds = platform_y_min <= object_pos[1] <= platform_y_max + z_in_bounds = object_pos[2] >= platform_z_min + + return bool(x_in_bounds and y_in_bounds and z_in_bounds) diff --git a/sumo/tasks/spot/spot_cone_push.py b/sumo/tasks/spot/spot_cone_push.py new file mode 100644 index 0000000..4b195e1 --- /dev/null +++ b/sumo/tasks/spot/spot_cone_push.py @@ -0,0 +1,171 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_traffic_cone.xml") + +USE_LEGS = False +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 +DEFAULT_CONE_HEIGHT = 0.0 + +DEFAULT_TORSO_POSITION = np.array([-1.75, 0, STANDING_HEIGHT]) +Z_AXIS = np.array([0.0, 0.0, 1.0]) +# Success condition tolerances +POSITION_TOLERANCE = 0.2 +VELOCITY_TOLERANCE = 0.05 + + +@dataclass +class SpotConePushConfig(SpotBaseConfig): + """Config for the spot cone pushing task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, DEFAULT_CONE_HEIGHT], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 1.0], + steps=[0.1, 0.1, 0.05], + vis_name="box_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[0.0, 0.0, DEFAULT_CONE_HEIGHT], + ) + + w_object_orientation: float = 100.0 + w_upright: float = 200.0 + upright_sparsity: float = 5.0 + w_gripper_proximity: float = 4.0 + w_torso_proximity: float = 0.1 + orientation_threshold: float = 0.7 + w_object_velocity: float = 20.0 + + +class SpotConePush(SpotBase): + """Task getting Spot to push a cone to a goal location.""" + + name = "spot_cone_push" + config_t = SpotConePushConfig + config: SpotConePushConfig + + def __init__(self, config: SpotConePushConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, config=config) + + self.body_pose_start = self.get_joint_position_start_index("base") + self.object_pose_start = self.get_joint_position_start_index("traffic_cone_joint") + self.object_vel_start = self.get_joint_velocity_start_index("traffic_cone_joint") + self.object_y_axis_start = self.get_sensor_start_index("object_y_axis") + self.object_z_axis_start = self.get_sensor_start_index("object_z_axis") + self.end_effector_to_object_start = self.get_sensor_start_index("sensor_arm_link_fngr") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot cone pushing task.""" + batch_size = states.shape[0] + + # (batch, horizon, size) + # or (batch, horizon) if scalar + qpos = states[..., : self.model.nq] + body_pos = qpos[..., self.body_pose_start : self.body_pose_start + 3] + object_pos = qpos[..., self.object_pose_start : self.object_pose_start + 3] + object_y_axis = sensors[..., self.object_y_axis_start : self.object_y_axis_start + 3] + object_z_axis = sensors[..., self.object_z_axis_start : self.object_z_axis_start + 3] + object_linear_velocity = states[..., self.object_vel_start : self.object_vel_start + 3] + + end_effector_to_object = sensors[..., self.end_effector_to_object_start : self.end_effector_to_object_start + 3] + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + end_effector_to_object, axis=-1 + ).mean(axis=-1) + + object_orientation_reward = -self.config.w_object_orientation * np.abs( + np.dot(object_y_axis, Z_AXIS) > self.config.orientation_threshold + ).sum(axis=-1) + + # Upright reward: incentivize cone z-axis aligned with world z-axis throughout the rollout + upright_alignment = np.minimum(np.dot(object_z_axis, Z_AXIS) - 1, 0.0) + upright_reward = self.config.w_upright * np.exp(self.config.upright_sparsity * upright_alignment).mean(axis=-1) + + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + torso_proximity_reward = self.config.w_torso_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + object_linear_velocity_penalty = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + # Check if any state in the rollout has spot fallen + body_height = qpos[..., self.body_pose_start + 2] + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Compute a penalty to prefer small commands. + controls_reward = -self.config.w_controls * np.linalg.norm(controls, axis=-1).mean(-1) + + assert object_orientation_reward.shape == (batch_size,) + assert upright_reward.shape == (batch_size,) + assert gripper_proximity_reward.shape == (batch_size,) + assert torso_proximity_reward.shape == (batch_size,) + assert object_linear_velocity_penalty.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + reward = ( + +spot_fallen_reward + + goal_reward + + object_orientation_reward + + upright_reward + + torso_proximity_reward + + gripper_proximity_reward + + object_linear_velocity_penalty + + controls_reward + ) + return reward + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + # Sample object position in annulus + radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + theta = 2 * np.pi * np.random.rand() + object_pos = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + 0.1 * np.random.randn(2) + + object_pose = np.array([*object_pos, DEFAULT_CONE_HEIGHT, 1, 0, 0, 0]) + + # Place robot at random x and y + robot_pose_xy = np.random.uniform(-0.5, 0.5, 2) + random_yaw_robot = np.random.uniform(0, 2 * np.pi) + robot_pose_orientation = np.array([np.cos(random_yaw_robot / 2), 0, 0, np.sin(random_yaw_robot / 2)]) + robot_pose = np.array([*robot_pose_xy, STANDING_HEIGHT, *robot_pose_orientation]) + + return np.array([*robot_pose, *LEGS_STANDING_POS, *self.reset_arm_pos, *object_pose]) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the cone is in the goal position.""" + object_pos = data.qpos[self.object_pose_start : self.object_pose_start + 3] + object_vel = data.qvel[self.object_vel_start - self.model.nq : self.object_vel_start - self.model.nq + 3] + goal_pos = np.array(self.config.goal_position) + position_check = np.linalg.norm(object_pos - goal_pos, axis=-1, ord=np.inf) < POSITION_TOLERANCE + velocity_check = np.linalg.norm(object_vel, axis=-1) < VELOCITY_TOLERANCE + return position_check and velocity_check + + def failure(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if Spot has fallen.""" + body_height = data.qpos[self.body_pose_start + 2] + return body_height <= self.config.spot_fallen_threshold diff --git a/sumo/tasks/spot/spot_cone_upright.py b/sumo/tasks/spot/spot_cone_upright.py new file mode 100644 index 0000000..c90b1cd --- /dev/null +++ b/sumo/tasks/spot/spot_cone_upright.py @@ -0,0 +1,165 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""SpotConeUpright task - upright a fallen traffic cone. + +The robot must pick up or nudge the cone back to an upright position. +""" + +from dataclasses import dataclass, field +from typing import Any + +import numpy as np +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_traffic_cone.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +RADIUS_MIN = 0.2 +RADIUS_MAX = 0.5 + +HARDWARE_FENCE_X = (-2.0, 3.0) +HARDWARE_FENCE_Y = (-3.0, 2.5) + +DEFAULT_SPOT_POS = np.array([-1.5, 0.0]) +DEFAULT_OBJECT_POS = np.array([0.0, 0.0]) + + +@dataclass +class SpotConeUprightConfig(SpotBaseConfig): + """Config for the spot cone upright task.""" + + goal_position: np.ndarray = field(default_factory=lambda: np.array([0.0, 0.0, 0.0])) + w_fence: float = 1000.0 + w_orientation: float = 50.0 + orientation_sparsity: float = 5.0 + w_torso_proximity: float = 50.0 + torso_proximity_threshold: float = 1.0 + w_gripper_proximity: float = 8.0 + orientation_threshold: float = 0.7 + w_controls: float = 2.0 + w_object_velocity: float = 64.0 + position_tolerance: float = 0.2 + orientation_tolerance: float = 0.1 + velocity_tolerance: float = 0.1 + + +class SpotConeUpright(SpotBase[SpotConeUprightConfig]): + """Task getting Spot to upright a fallen traffic cone.""" + + name: str = "spot_cone_upright" + config_t: type[SpotConeUprightConfig] = SpotConeUprightConfig # type: ignore[assignment] + config: SpotConeUprightConfig + + def __init__(self, config: SpotConeUprightConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, config=config) + + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("traffic_cone_joint") + self.object_vel_idx = self.get_joint_velocity_start_index("traffic_cone_joint") + self.object_y_axis_idx = self.get_sensor_start_index("object_y_axis") + self.object_z_axis_idx = self.get_sensor_start_index("object_z_axis") + self.end_effector_to_object_idx = self.get_sensor_start_index("sensor_arm_link_fngr") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the cone upright task.""" + batch_size = states.shape[0] + + qpos = states[..., : self.model.nq] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + object_linear_velocity = states[..., self.object_vel_idx : self.object_vel_idx + 3] + + object_z_axis = sensors[..., self.object_z_axis_idx : self.object_z_axis_idx + 3] + gripper_to_object = sensors[..., self.end_effector_to_object_idx : self.end_effector_to_object_idx + 3] + + fence_violated_x = (body_pos[..., 0] < HARDWARE_FENCE_X[0]) | (body_pos[..., 0] > HARDWARE_FENCE_X[1]) + fence_violated_y = (body_pos[..., 1] < HARDWARE_FENCE_Y[0]) | (body_pos[..., 1] > HARDWARE_FENCE_Y[1]) + spot_fence_reward = -self.config.w_fence * (fence_violated_x | fence_violated_y).any(axis=-1) + + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + orientation_alignment = np.minimum(np.dot(object_z_axis, Z_AXIS) - 1, self.config.orientation_threshold) + object_orientation_reward = +self.config.w_orientation * np.exp( + self.config.orientation_sparsity * orientation_alignment + ).sum(axis=-1) + + torso_proximity_reward = self.config.w_torso_proximity * np.minimum( + self.config.torso_proximity_threshold, np.linalg.norm(body_pos - object_pos, axis=-1) + ).mean(-1) + + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + gripper_to_object, + axis=-1, + ).mean(-1) + + object_linear_velocity_reward = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + assert spot_fence_reward.shape == (batch_size,) + return ( + spot_fence_reward + + spot_fallen_reward + + goal_reward + + object_orientation_reward + + torso_proximity_reward + + gripper_proximity_reward + + object_linear_velocity_reward + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose — cone starts fallen on its side.""" + radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + theta = 2 * np.pi * np.random.rand() + object_pos = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + np.random.randn(2) + # Cone on its side: 90-degree roll about x-axis + reset_object_pose = np.array([*object_pos, 0.275, np.cos(np.pi / 4), -np.sin(np.pi / 4), 0, 0]) + spot_pos = DEFAULT_SPOT_POS + np.random.randn(2) * 0.001 + return np.array( + [ + *spot_pos, + STANDING_HEIGHT, + 1, + 0, + 0, + 0, + *LEGS_STANDING_POS, + *self.reset_arm_pos, + *reset_object_pose, + ] + ) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the traffic cone is upright.""" + object_z_axis = data.sensordata[self.object_z_axis_idx : self.object_z_axis_idx + 3] + orientation_alignment = np.dot(object_z_axis, Z_AXIS) + orientation_success = orientation_alignment >= (1.0 - self.config.orientation_tolerance) + + vel_offset = self.object_vel_idx - self.model.nq + velocity_success = ( + np.linalg.norm(data.qvel[vel_offset : vel_offset + 3], axis=-1) < self.config.velocity_tolerance + ) + return bool(orientation_success and velocity_success) diff --git a/sumo/tasks/spot/spot_constants.py b/sumo/tasks/spot/spot_constants.py new file mode 100644 index 0000000..b50ade6 --- /dev/null +++ b/sumo/tasks/spot/spot_constants.py @@ -0,0 +1,518 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. +from dataclasses import dataclass +from enum import Enum, IntEnum + +import numpy as np + +# Import shared constants from judo-rai +from judo.tasks.spot.spot_constants import ( # noqa: F401 + ARM_JOINT_NAMES, + ARM_STOWED_POS, + ARM_UNSTOWED_POS, + GRIPPER_CLOSED_POS, + GRIPPER_OPEN_POS, + LEG_JOINT_NAMES_BOSDYN, + LEGS_STANDING_POS, + LEGS_STANDING_POS_RL, + STANDING_HEIGHT, + TIRE_HALF_WIDTH, + TIRE_RADIUS, + Z_AXIS, +) + +LEG_JOINT_NAMES_ISAAC = [ + "fl_hx", + "fr_hx", + "hl_hx", + "hr_hx", + "fl_hy", + "fr_hy", + "hl_hy", + "hr_hy", + "fl_kn", + "fr_kn", + "hl_kn", + "hr_kn", +] + +JOINT_NAMES_BOSDYN = LEG_JOINT_NAMES_BOSDYN + ARM_JOINT_NAMES + +JOINT_NAMES_ISAAC = [ + "arm_sh0", + "fl_hx", + "fr_hx", + "hl_hx", + "hr_hx", + "arm_sh1", + "fl_hy", + "fr_hy", + "hl_hy", + "hr_hy", + "arm_el0", + "fl_kn", + "fr_kn", + "hl_kn", + "hr_kn", + "arm_el1", + "arm_wr0", + "arm_wr1", + "arm_f1x", +] + +LEG_NAMES = [ + "front_left", + "front_right", + "rear_left", + "rear_right", +] + +FEET_NAMES = [f"{leg_name}_foot" for leg_name in LEG_NAMES] + +FL_JOINT_NAMES = ["fl_hx", "fl_hy", "fl_kn"] +FR_JOINT_NAMES = ["fr_hx", "fr_hy", "fr_kn"] +HL_JOINT_NAMES = ["hl_hx", "hl_hy", "hl_kn"] +HR_JOINT_NAMES = ["hr_hx", "hr_hy", "hr_kn"] +LEG_JOINT_NAMES_DICT = { + "fl": FL_JOINT_NAMES, + "fr": FR_JOINT_NAMES, + "hl": HL_JOINT_NAMES, + "hr": HR_JOINT_NAMES, +} + + +def get_joint_names_bosdyn(has_arm: bool) -> list: + """Return the list of Spot joint names in BD API order. + + Args: + has_arm (bool): If true, include the arm joints. + """ + if has_arm: + return JOINT_NAMES_BOSDYN + return LEG_JOINT_NAMES_BOSDYN + + +def get_joint_names_isaac(has_arm: bool) -> list: + """Return the list of Spot joint names in Isaac order. + + Args: + has_arm (bool): If true, include the arm joints. + """ + if has_arm: + return JOINT_NAMES_ISAAC + return LEG_JOINT_NAMES_ISAAC + + +### Camera-related names +CAMERA_IMAGE_SOURCES = [ + "frontleft_fisheye_image", + "frontright_fisheye_image", + "left_fisheye_image", + "right_fisheye_image", + "back_fisheye_image", + "hand_color_image", +] +DEPTH_IMAGE_SOURCES = [ + "frontleft_depth", + "frontright_depth", + "left_depth", + "right_depth", + "back_depth", + "hand_depth", +] +DEPTH_REGISTERED_IMAGE_SOURCES = [ + "frontleft_depth_in_visual_frame", + "frontright_depth_in_visual_frame", + "right_depth_in_visual_frame", + "left_depth_in_visual_frame", + "back_depth_in_visual_frame", + "hand_depth_in_hand_color_frame", +] + +IMAGE_TYPES = {"visual", "depth", "depth_registered"} + + +### Enums and DataClasses +class DOF(IntEnum): + """Link index and order""" + + # FL_HX = spot_constants_pb2.JOINT_INDEX_FL_HX + # FL_HY = spot_constants_pb2.JOINT_INDEX_FL_HY + # FL_KN = spot_constants_pb2.JOINT_INDEX_FL_KN + # FR_HX = spot_constants_pb2.JOINT_INDEX_FR_HX + # FR_HY = spot_constants_pb2.JOINT_INDEX_FR_HY + # FR_KN = spot_constants_pb2.JOINT_INDEX_FR_KN + # HL_HX = spot_constants_pb2.JOINT_INDEX_HL_HX + # HL_HY = spot_constants_pb2.JOINT_INDEX_HL_HY + # HL_KN = spot_constants_pb2.JOINT_INDEX_HL_KN + # HR_HX = spot_constants_pb2.JOINT_INDEX_HR_HX + # HR_HY = spot_constants_pb2.JOINT_INDEX_HR_HY + # HR_KN = spot_constants_pb2.JOINT_INDEX_HR_KN + # # Arm + # A0_SH0 = spot_constants_pb2.JOINT_INDEX_A0_SH0 + # A0_SH1 = spot_constants_pb2.JOINT_INDEX_A0_SH1 + # A0_EL0 = spot_constants_pb2.JOINT_INDEX_A0_EL0 + # A0_EL1 = spot_constants_pb2.JOINT_INDEX_A0_EL1 + # A0_WR0 = spot_constants_pb2.JOINT_INDEX_A0_WR0 + # A0_WR1 = spot_constants_pb2.JOINT_INDEX_A0_WR1 + # # Hand + # A0_F1X = spot_constants_pb2.JOINT_INDEX_A0_F1X + + # DOF count for strictly the legs. + N_DOF_LEGS = 12 + # DOF count for all DOF on robot (arms and legs). + N_DOF = 19 + + +class LEGS(IntEnum): + """Leg links index and order""" + + # FL = spot_constants_pb2.LEG_INDEX_FL + # FR = spot_constants_pb2.LEG_INDEX_FR + # HL = spot_constants_pb2.LEG_INDEX_HL + # HR = spot_constants_pb2.LEG_INDEX_HR + + N_LEGS = 4 + + +class LegDofOrder(IntEnum): + """Legs DoF order""" + + # HX = spot_constants_pb2.HX + # HY = spot_constants_pb2.HY + # KN = spot_constants_pb2.KN + + # The number of leg dof + N_LEG_DOF = 3 + + +@dataclass +class POLICY_MODE: + """Dataclass containing the two supported policy modes""" + + boston_dynamics: str = "boston_dynamics" + rl_locomotion: str = "rl_locomotion" + + +### Gains +# Bosdyn +LEG_K_Q_P_BOSDYN = [624, 936, 286.0] * LEGS.N_LEGS +LEG_K_QD_P_BOSDYN = [5.20, 5.20, 2.04] * LEGS.N_LEGS +ARM_K_Q_P_BOSDYN = [1020, 255, 204, 102, 102, 102, 16.0] +ARM_K_QD_P_BOSDYN = [10.2, 15.3, 10.2, 2.04, 2.04, 2.04, 0.32] + +# RL locomotion policy during training +LEG_K_Q_P_RL = [60, 60, 60.0] * LEGS.N_LEGS +LEG_K_QD_P_RL = [1.5, 1.5, 1.5] * LEGS.N_LEGS +ARM_K_Q_P_RL = [120, 120, 120, 100, 100, 100, 16.0] +ARM_K_QD_P_RL = [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.32] +K_Q_P_RL = LEG_K_Q_P_RL + ARM_K_Q_P_RL +K_QD_P_RL = LEG_K_QD_P_RL + ARM_K_QD_P_RL + +# RL locomotion policy on hardware +LEG_K_Q_P_HW = [75, 75, 75.0] * LEGS.N_LEGS +LEG_K_QD_P_HW = LEG_K_QD_P_RL +ARM_K_Q_P_HW = ARM_K_Q_P_RL +ARM_K_QD_P_HW = ARM_K_QD_P_RL + +### Initial Configurations +# Standard joint positions +LEGS_SITTING_POS = np.array( + [ + 0.48, + 1.26, + -2.7929, + -0.48, + 1.26, + -2.7929, + 0.48, + 1.26, + -2.7929, + -0.48, + 1.26, + -2.7929, + ] +) + +ARM_MIDDLE_POS = np.array( + [ + 0, + -2.8, + 2.8, + 1.56, + 0, + -1.56, + GRIPPER_CLOSED_POS, + ] +) + +SITTING_STOWED_POS = np.concatenate((LEGS_SITTING_POS, ARM_STOWED_POS)) + +SITTING_UNSTOWED_POS = np.concatenate((LEGS_SITTING_POS, ARM_UNSTOWED_POS)) + +STANDING_STOWED_POS = np.concatenate((LEGS_STANDING_POS, ARM_STOWED_POS)) + +STANDING_UNSTOWED_POS = np.concatenate((LEGS_STANDING_POS, ARM_UNSTOWED_POS)) + +STANDING_POS_RL = np.concatenate((LEGS_STANDING_POS_RL, ARM_UNSTOWED_POS[:-1], np.array([GRIPPER_OPEN_POS]))) + +SITTING_HEIGHT = 0.1 +STANDING_HEIGHT_CMD = 0.55 + +### Limits +# RL locomotion policy limits +LEG_TORQUE_LIMITS_RL = [45.0, 45.0, 60.0] * LEGS.N_LEGS +ARM_TORQUE_LIMITS_RL = [90.9, 181.8, 90.9, 30.3, 30.3, 30.3, 15.32] +TORQUE_LIMITS_RL = LEG_TORQUE_LIMITS_RL + ARM_TORQUE_LIMITS_RL + +# position limits +LEG_LOWER_JOINT_LIMITS = np.array([-0.785398, -0.898845, -2.7929] * LEGS.N_LEGS) +LEG_UPPER_JOINT_LIMITS = np.array([0.785398, 2.29511, -0.254801] * LEGS.N_LEGS) +ARM_LOWER_JOINT_LIMITS = np.array([-2.618, -3.1416, 0, -2.7925, -1.8326, -2.8798, -1.57]) +ARM_UPPER_JOINT_LIMITS = np.array([3.1416, 0.5236, 3.1416, 2.7925, 1.8326, 2.8798, 0]) +LOWER_JOINT_LIMITS = np.concatenate((LEG_LOWER_JOINT_LIMITS, ARM_LOWER_JOINT_LIMITS)) +UPPER_JOINT_LIMITS = np.concatenate((LEG_UPPER_JOINT_LIMITS, ARM_UPPER_JOINT_LIMITS)) + +# velocity limits +VELOCITY_TASK_SAFETY_LIMIT: float = 30.0 # Limit when task execution should be stopped +VELOCITY_HW_SAFETY_LIMIT: float = 40.0 # Limit when Spot will shut down + +# Command lengths +# velocity_cmd: 3, arm_joint_cmd: 7, leg_joint_cmd: 12, torso_rph: 3 --> total = 25 +RL_LOCOMOTION_COMMAND_LENGTH = 25 +RL_LOCOMOTION_ACTION_LENGTH = 12 + +SECOND_TO_NANOSECOND = 1_000_000_000 + + +### Target info +# For Robot IPs from, see: +# https://www.notion.so/theaiinstitute/Spot-Passwords-and-Network-Info-09fb3beacb6440d1bcc3677ae532307c + +P2P_IP_ADDRESS = "192.168.50.3" + + +@dataclass +class TargetInfo: + """Information for targets to run policies on""" + + mocap_id: int + eth_ip: str = "" + p2p_ip: str = P2P_IP_ADDRESS + + def __post_init__(self) -> None: + if self.eth_ip == "": + self.eth_ip = f"10.0.0.{self.mocap_id}" + + +class ValidTargets(Enum): + """Targets to run policies on""" + + # Mujoco Mocap ID for needed for rendering. IP address is placeholder. + mujoco = TargetInfo(mocap_id=100) + quinn = TargetInfo(mocap_id=101) + fausto = TargetInfo(mocap_id=102) + seashell = TargetInfo(mocap_id=103) + gosu = TargetInfo(mocap_id=104) + zephyr = TargetInfo(mocap_id=105) + lionel = TargetInfo(mocap_id=106) + bard = TargetInfo(mocap_id=107) + antora = TargetInfo(mocap_id=108) + + @classmethod + def is_valid_target(cls, target: str) -> bool: + """Check if string is valid target""" + return target in cls.__members__ + + @classmethod + def is_valid_mocap_id(cls, mocap_id: int) -> bool: + """Check if string is valid target""" + return mocap_id in [cls[target].value.mocap_id for target in cls.__members__] + + @classmethod + def helptext(cls) -> str: + """Help text for ValidTarget command line argument.""" + return f"Robot target. Choose from:\n [{', '.join([x.name for x in cls])}]" + + +### Isaac <-> Mujoco conversion +ISAAC_TO_MUJOCO_INDICES_12 = [0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11] +ISAAC_TO_MUJOCO_INDICES_19 = [1, 6, 11, 2, 7, 12, 3, 8, 13, 4, 9, 14, 0, 5, 10, 15, 16, 17, 18] +MUJOCO_TO_ISAAC_INDICES_12 = [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11] +MUJOCO_TO_ISAAC_INDICES_19 = [12, 0, 3, 6, 9, 13, 1, 4, 7, 10, 14, 2, 5, 8, 11, 15, 16, 17, 18] + + +def isaac_to_mujoco(var_isaac: np.ndarray) -> np.ndarray: + """Reorders an array from Isaac Gym to MuJoCo format. + + Args: + var_isaac (np.ndarray): Input array in Isaac Gym format. + + Returns: + np.ndarray: Array reordered for MuJoCo. + """ + if len(var_isaac) == 12: + isaac_to_mujoco_list = ISAAC_TO_MUJOCO_INDICES_12 + elif len(var_isaac) == 19: + isaac_to_mujoco_list = ISAAC_TO_MUJOCO_INDICES_19 + else: + raise ValueError( + f"The vector must of size 12 (legs only) or 19 (arm + legs), size of given vector {var_isaac.shape}" + ) + var_mujoco = var_isaac[isaac_to_mujoco_list] + return var_mujoco + + +def mujoco_to_isaac(var_mujoco: np.ndarray) -> np.ndarray: + """Reorders an array from MuJoCo to Isaac Gym format. + + Args: + var_mujoco (np.ndarray): Input array in MuJoCo format. + + Returns: + np.ndarray: Array reordered for Isaac Gym. + """ + if len(var_mujoco) == 12: + mujoco_to_isaac_list = MUJOCO_TO_ISAAC_INDICES_12 + elif len(var_mujoco) == 19: + mujoco_to_isaac_list = MUJOCO_TO_ISAAC_INDICES_19 + else: + raise ValueError( + f"The vector must of size 12 (legs only) or 19 (arm + legs), size of given vector {var_mujoco.shape}" + ) + var_isaac = var_mujoco[mujoco_to_isaac_list] + return var_isaac + + +### Spot and object states +def _slice_union(*slices: slice) -> slice: + """Create a union of multiple slices, ensuring they are contiguous.""" + # Sort slices based on their start values + sorted_slices = sorted(slices, key=lambda s: s.start) + + start = sorted_slices[0].start + stop = sorted_slices[-1].stop + + # Check for contiguity + for i in range(len(sorted_slices) - 1): + if sorted_slices[i].stop != sorted_slices[i + 1].start: + raise ValueError("Slices are not contiguous") + + return slice(start, stop) + + +def _non_contiguous_slices_to_indices(*slices: slice) -> list[int]: + """Create a union of multiple slices that are not contiguous.""" + # Sort slices based on their start values + sorted_slices = sorted(slices, key=lambda s: s.start) + indices: list[int] = [] + for s in sorted_slices: + indices.extend(range(s.start, s.stop)) + return indices + + +@dataclass(frozen=True) +class SpotObjectStateArray: + """State array indexing helper for spot object MuJoCo state.""" + + # TODO: make SceneStateArray - a dynamic indexing helper based on the loaded model + + # Position slices + base_pos = slice(0, 3) + base_quat = slice(3, 7) + fl_leg = slice(7, 10) + fr_leg = slice(10, 13) + hl_leg = slice(13, 16) + hr_leg = slice(16, 19) + arm = slice(19, 26) + object_pos = slice(26, 29) + object_quat = slice(29, 33) + + # Velocity slices + base_lin_vel = slice(33, 36) + base_ang_vel = slice(36, 39) + fl_leg_vel = slice(39, 42) + fr_leg_vel = slice(42, 45) + hl_leg_vel = slice(45, 48) + hr_leg_vel = slice(48, 51) + arm_vel = slice(51, 58) + object_lin_vel = slice(58, 61) + object_ang_vel = slice(61, 64) + + ## Unions + # Positions + base_pose = _slice_union(base_pos, base_quat) + legs = _slice_union(fl_leg, fr_leg, hl_leg, hr_leg) + joint_pos = _slice_union(legs, arm) + object_pose = _slice_union(object_pos, object_quat) + qpos = _slice_union(base_pose, joint_pos, object_pose) + # Velocities + base_vel = _slice_union(base_lin_vel, base_ang_vel) + legs_vel = _slice_union(fl_leg_vel, fr_leg_vel, hl_leg_vel, hr_leg_vel) + joint_vel = _slice_union(legs_vel, arm_vel) + object_vel = _slice_union(object_lin_vel, object_ang_vel) + qvel = _slice_union(base_vel, joint_vel, object_vel) + # Full States + spot_state = _non_contiguous_slices_to_indices(base_pose, joint_pos, base_vel, joint_vel) + object_state = _non_contiguous_slices_to_indices(object_pose, object_vel) + + size = 64 + + +# TODO(tong): Is this even used anywhere? Why is this still around? +@dataclass(frozen=True) +class SpotStateArray: + """State array indexing helper for spot MuJoCo state.""" + + # TODO: make SceneStateArray - a dynamic indexing helper based on the loaded model + + # Position slices + base_pos = slice(0, 3) + base_quat = slice(3, 7) + fl_leg = slice(7, 10) + fr_leg = slice(10, 13) + hl_leg = slice(13, 16) + hr_leg = slice(16, 19) + arm = slice(19, 26) + + # Velocity slices + base_lin_vel = slice(26, 29) + base_ang_vel = slice(29, 32) + fl_leg_vel = slice(32, 35) + fr_leg_vel = slice(35, 38) + hl_leg_vel = slice(38, 41) + hr_leg_vel = slice(41, 44) + arm_vel = slice(44, 51) + + ## Unions + # Positions + base_pose = _slice_union(base_pos, base_quat) + legs = _slice_union(fl_leg, fr_leg, hl_leg, hr_leg) + joint_pos = _slice_union(legs, arm) + qpos = _slice_union(base_pose, joint_pos) + # Velocities + base_vel = _slice_union(base_lin_vel, base_ang_vel) + legs_vel = _slice_union(fl_leg_vel, fr_leg_vel, hl_leg_vel, hr_leg_vel) + joint_vel = _slice_union(legs_vel, arm_vel) + qvel = _slice_union(base_vel, joint_vel) + # Full States + spot_state = _slice_union(qpos, qvel) + + # Object and Goal None values for type checking + object_pos = None + object_quat = None + object_vel = None + + size = 51 + + +# Foxglove joint states update schema +joint_state_json_schema = { + "type": "object", + "properties": { + "name": {"type": "array", "items": {"type": "string"}}, + "position": {"type": "array", "items": {"type": "number"}}, + "velocity": {"type": "array", "items": {"type": "number"}}, + "effort": {"type": "array", "items": {"type": "number"}}, + }, + "required": ["name", "position", "velocity", "effort"], +} diff --git a/sumo/tasks/spot/spot_rack_push.py b/sumo/tasks/spot/spot_rack_push.py new file mode 100644 index 0000000..f9c4156 --- /dev/null +++ b/sumo/tasks/spot/spot_rack_push.py @@ -0,0 +1,162 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_tire_rack.xml") + +USE_LEGS = False +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 +DEFAULT_RACK_HEIGHT = 0.3 + +DEFAULT_TORSO_POSITION = np.array([-1.75, 0, STANDING_HEIGHT]) +Z_AXIS = np.array([0.0, 0.0, 1.0]) +# Success condition tolerances +POSITION_TOLERANCE = 0.2 +VELOCITY_TOLERANCE = 0.05 + + +@dataclass +class SpotRackPushConfig(SpotBaseConfig): + """Config for the spot rack pushing task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, DEFAULT_RACK_HEIGHT], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 1.0], + steps=[0.1, 0.1, 0.05], + vis_name="box_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[0.0, 0.0, DEFAULT_RACK_HEIGHT], + ) + + w_object_orientation: float = 100.0 + w_gripper_proximity: float = 4.0 + w_torso_proximity: float = 0.1 + orientation_threshold: float = 0.7 + w_object_velocity: float = 20.0 + + +class SpotRackPush(SpotBase): + """Task getting Spot to push a rack to a goal location.""" + + name = "spot_rack_push" + config_t = SpotRackPushConfig + config: SpotRackPushConfig + + def __init__(self, config: SpotRackPushConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, config=config) + + self.body_pose_start = self.get_joint_position_start_index("base") + self.object_pose_start = self.get_joint_position_start_index("tire_rack_joint") + self.object_vel_start = self.get_joint_velocity_start_index("tire_rack_joint") + self.object_y_axis_start = self.get_sensor_start_index("object_y_axis") + self.end_effector_to_object_start = self.get_sensor_start_index("sensor_arm_link_fngr") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot rack pushing task.""" + batch_size = states.shape[0] + + # (batch, horizon, size) + # or (batch, horizon) if scalar + qpos = states[..., : self.model.nq] + body_pos = qpos[..., self.body_pose_start : self.body_pose_start + 3] + object_pos = qpos[..., self.object_pose_start : self.object_pose_start + 3] + object_y_axis = sensors[..., self.object_y_axis_start : self.object_y_axis_start + 3] + object_linear_velocity = states[..., self.object_vel_start : self.object_vel_start + 3] + + # Compute unit vector pointing from tire to torso + end_effector_to_object = sensors[..., self.end_effector_to_object_start : self.end_effector_to_object_start + 3] + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + end_effector_to_object, axis=-1 + ).mean(axis=-1) + + object_orientation_reward = -self.config.w_object_orientation * np.abs( + np.dot(object_y_axis, Z_AXIS) > self.config.orientation_threshold + ).sum(axis=-1) + + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + torso_proximity_reward = self.config.w_torso_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + object_linear_velocity_penalty = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + # Check if any state in the rollout has spot fallen + body_height = qpos[..., self.body_pose_start + 2] + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Compute a penalty to prefer small commands. + controls_reward = -self.config.w_controls * np.linalg.norm(controls, axis=-1).mean(-1) + + assert object_orientation_reward.shape == (batch_size,) + assert gripper_proximity_reward.shape == (batch_size,) + assert torso_proximity_reward.shape == (batch_size,) + assert object_linear_velocity_penalty.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + reward = ( + +spot_fallen_reward + + goal_reward + + object_orientation_reward + + torso_proximity_reward + + gripper_proximity_reward + + object_linear_velocity_penalty + + controls_reward + ) + return reward + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + # Sample object position in annulus + radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + theta = 2 * np.pi * np.random.rand() + object_pos = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + 0.1 * np.random.randn(2) + + object_pose = np.array([*object_pos, DEFAULT_RACK_HEIGHT, 1, 0, 0, 0]) + + # Place robot at random x and y + robot_pose_xy = np.random.uniform(-0.5, 0.5, 2) + random_yaw_robot = np.random.uniform(0, 2 * np.pi) + robot_pose_orientation = np.array([np.cos(random_yaw_robot / 2), 0, 0, np.sin(random_yaw_robot / 2)]) + robot_pose = np.array([*robot_pose_xy, STANDING_HEIGHT, *robot_pose_orientation]) + + return np.array([*robot_pose, *LEGS_STANDING_POS, *self.reset_arm_pos, *object_pose]) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the rack is in the goal position.""" + object_pos = data.qpos[self.object_pose_start : self.object_pose_start + 3] + object_vel = data.qvel[self.object_vel_start - self.model.nq : self.object_vel_start - self.model.nq + 3] + goal_pos = np.array(self.config.goal_position) + position_check = np.linalg.norm(object_pos - goal_pos, axis=-1, ord=np.inf) < POSITION_TOLERANCE + velocity_check = np.linalg.norm(object_vel, axis=-1) < VELOCITY_TOLERANCE + return position_check and velocity_check + + def failure(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if Spot has fallen.""" + body_height = data.qpos[self.body_pose_start + 2] + return body_height <= self.config.spot_fallen_threshold diff --git a/sumo/tasks/spot/spot_rugged_box_push.py b/sumo/tasks/spot/spot_rugged_box_push.py new file mode 100644 index 0000000..a586588 --- /dev/null +++ b/sumo/tasks/spot/spot_rugged_box_push.py @@ -0,0 +1,243 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_rugged_box.xml") + +X_AXIS = np.array([1.0, 0.0, 0.0]) +Y_AXIS = np.array([0.0, 1.0, 0.0]) +Z_AXIS = np.array([0.0, 0.0, 1.0]) + +# annulus object position sampling +RADIUS_MIN = 1.5 +RADIUS_MAX = 3.0 +USE_LEGS = False +USE_GRIPPER = True +USE_TORSO = True + +HARDWARE_FENCE_X = (-2.0, 3.0) +HARDWARE_FENCE_Y = (-3.0, 2.5) + +DEFAULT_SPOT_POS = np.array([0.0, 0.0]) +DEFAULT_OBJECT_POS = np.array([3.0, 0.0]) + + +@dataclass +class SpotRuggedBoxPushConfig(SpotBaseConfig): + """Config for the spot rugged box push task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([-0.0, 0.0, 0.0], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, -1.0], + maxs=[5.0, 5.0, 2.0], + steps=[0.1, 0.1, 0.1], + vis_name="rugged_box_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[-1.0, 1.3, 0.0], + ) + w_fence: float = 1000.0 + w_goal: float = 500.0 + w_orientation: float = 50.0 + w_torso_proximity: float = 30.0 + w_gripper_proximity: float = 0.1 + w_controls: float = 2.0 + orientation_threshold: float = 0.5 + torso_distance_from_box: float = 0.6 # Distance from box for desired torso position + torso_desired_height: float = 0.4 # Desired height for torso position + torso_height_upper_bound: float = 0.4 # Upper bound for torso height control + position_tolerance: float = 0.2 + + +class SpotRuggedBoxPush(SpotBase): + """Task getting Spot to push a rugged box to a desired goal location.""" + + name = "spot_rugged_box_push" + config_t = SpotRuggedBoxPushConfig + config: SpotRuggedBoxPushConfig + + def __init__(self, config: SpotRuggedBoxPushConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, use_gripper=True, use_torso=True, config=config) + + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("rugged_box_joint") + self.object_vel_idx = self.get_joint_velocity_start_index("rugged_box_joint") + + # Object orientation sensors + self.object_x_axis_idx = self.get_sensor_start_index("object_x_axis") + self.object_y_axis_idx = self.get_sensor_start_index("object_y_axis") + self.object_z_axis_idx = self.get_sensor_start_index("object_z_axis") + + # End effector to object sensor + self.end_effector_to_object_idx = self.get_sensor_start_index("sensor_arm_link_fngr") + + @property + def actuator_ctrlrange(self) -> np.ndarray: + """Control bounds for the task with custom torso height upper bound. + + Override to set a lower upper bound for torso height control. + """ + # Get the base control range from parent class + base_ctrlrange = super().actuator_ctrlrange + + # Modify the torso height upper bound (last element in the upper bound) + # The torso height is the last element if use_torso is True + if self.use_torso: + # Get custom height from config if available, otherwise use default + custom_torso_height_upper = 0.4 + base_ctrlrange[-1, 1] = custom_torso_height_upper # Modify upper bound of last element + + return base_ctrlrange + + def set_command_values(self) -> None: + """Update default_command with custom torso target height.""" + super().set_command_values() + # Set custom torso target height (default is STANDING_HEIGHT, range is 0.3 to 1.0) + # Torso command format: [roll, pitch, height] + if self.use_torso: + self.default_command[-3:] = [0.0, 0.0, 0.4] # Set height to 0.4 + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot rugged box moving task.""" + batch_size = states.shape[0] + + qpos = states[..., : self.model.nq] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + + # Sensors sampled at the same horizon as states + object_x_axis = sensors[..., self.object_x_axis_idx : self.object_x_axis_idx + 3] + object_y_axis = sensors[..., self.object_y_axis_idx : self.object_y_axis_idx + 3] + object_z_axis = sensors[..., self.object_z_axis_idx : self.object_z_axis_idx + 3] + end_effector_to_object = sensors[..., self.end_effector_to_object_idx : self.end_effector_to_object_idx + 3] + + # Fence reward + fence_violated_x = (body_pos[..., 0] < HARDWARE_FENCE_X[0]) | (body_pos[..., 0] > HARDWARE_FENCE_X[1]) + fence_violated_y = (body_pos[..., 1] < HARDWARE_FENCE_Y[0]) | (body_pos[..., 1] > HARDWARE_FENCE_Y[1]) + spot_fence_reward = -self.config.w_fence * (fence_violated_x | fence_violated_y).any(axis=-1) + + # Check if spot has fallen + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Goal reward - distance to goal position + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + # Penalize deviation from desired orientation (all three axes) + # Box x-axis should align with world X_AXIS + # Box y-axis should align with world Y_AXIS + # Box z-axis should align with world Z_AXIS + x_alignment = np.abs(1.0 - np.sum(object_x_axis * X_AXIS, axis=-1)) + y_alignment = np.abs(1.0 - np.sum(object_y_axis * Y_AXIS, axis=-1)) + z_alignment = np.abs(1.0 - np.sum(object_z_axis * Z_AXIS, axis=-1)) + + box_orientation_reward = -self.config.w_orientation * (x_alignment + y_alignment + z_alignment).mean(axis=-1) + + # Compute desired torso position: distance away from box in opposite direction of goal (2D) + goal_position = np.array(self.config.goal_position) + # Direction from box to goal (2D) + box_to_goal_2d = goal_position[None, None, :2] - object_pos[..., :2] + box_to_goal_norm = np.maximum(np.linalg.norm(box_to_goal_2d, axis=-1, keepdims=True), 1e-6) + box_to_goal_unit = box_to_goal_2d / box_to_goal_norm + + # Desired torso position: opposite direction from box to goal + distance_from_box = self.config.torso_distance_from_box + torso_desired_xy = object_pos[..., :2] - distance_from_box * box_to_goal_unit + torso_desired = np.concatenate([torso_desired_xy, object_pos[..., 2:3]], axis=-1) + torso_desired[..., 2] = self.config.torso_desired_height + + # Compute l2 distance from torso pos. to desired torso pos. + torso_proximity_reward = -self.config.w_torso_proximity * np.linalg.norm( + body_pos - torso_desired, axis=-1 + ).mean(-1) + + # Compute l2 distance from end effector to object + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + end_effector_to_object, + axis=-1, + ).mean(-1) + + # Control penalty + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + assert spot_fence_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert box_orientation_reward.shape == (batch_size,) + assert torso_proximity_reward.shape == (batch_size,) + assert gripper_proximity_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + return ( + # spot_fence_reward + +spot_fallen_reward + + goal_reward + + box_orientation_reward + + torso_proximity_reward + + gripper_proximity_reward + + controls_reward + ) + + # @property + # def reset_pose(self) -> np.ndarray: + # """Reset pose of robot and object.""" + + # # Sample object position in annulus + # radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + # theta = 2 * np.pi * np.random.rand() + # object_pos = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + 0.1 * np.random.randn(2) + + # object_pose = np.array([*object_pos, 0.254, 1, 0, 0, 0]) + + # # Place robot at random x and y + # robot_pose_xy = np.random.uniform(-0.5, 0.5, 2) + # random_yaw_robot = np.random.uniform(0, 2 * np.pi) + # robot_pose_orientation = np.array([np.cos(random_yaw_robot / 2), 0, 0, np.sin(random_yaw_robot / 2)]) + # robot_pose = np.array([*robot_pose_xy, STANDING_HEIGHT, *robot_pose_orientation]) + + # return np.array([*robot_pose, *LEGS_STANDING_POS, *self.reset_arm_pos, *object_pose]) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + object_pos = np.array([0.5, 2.0]) + object_pose = np.array([*object_pos, 0.0, 1, 0, 0, 0]) + + # Place robot at random x and y + robot_pose_xy = np.array([1.0, 0.0]) + robot_pose_orientation = np.array([1, 0, 0, 0]) + robot_pose = np.array([*robot_pose_xy, STANDING_HEIGHT, *robot_pose_orientation]) + + return np.array([*robot_pose, *LEGS_STANDING_POS, *self.reset_arm_pos, *object_pose]) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the rugged box has been successfully pushed to the goal position.""" + object_pos = data.qpos[self.object_pose_idx : self.object_pose_idx + 3] + goal_position = np.array(self.config.goal_position) + + # Check position tolerance + distance_to_goal = np.linalg.norm(object_pos - goal_position) + position_success = distance_to_goal <= self.config.position_tolerance + + return bool(position_success) diff --git a/sumo/tasks/spot/spot_tire_push.py b/sumo/tasks/spot/spot_tire_push.py new file mode 100644 index 0000000..8209417 --- /dev/null +++ b/sumo/tasks/spot/spot_tire_push.py @@ -0,0 +1,163 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_tire.xml") + +TIRE_RADIUS: float = 0.339 +TIRE_WIDTH: float = 0.175 +USE_LEGS = False +RADIUS_MIN = 1.0 +RADIUS_MAX = 2.0 + +DEFAULT_TORSO_POSITION = np.array([-1.75, 0, STANDING_HEIGHT]) +Z_AXIS = np.array([0.0, 0.0, 1.0]) +# Success condition tolerances +POSITION_TOLERANCE = 0.1 +VELOCITY_TOLERANCE = 0.05 + + +@dataclass +class SpotTirePushConfig(SpotBaseConfig): + """Config for the spot tire pushing task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([0.0, 0.0, TIRE_RADIUS], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, 0.0], + maxs=[5.0, 5.0, 1.0], + steps=[0.1, 0.1, 0.05], + vis_name="box_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[0.0, 0.0, TIRE_RADIUS], + ) + + w_tire_orientation: float = 100.0 + w_gripper_proximity: float = 4.0 + w_torso_proximity: float = 0.1 + orientation_threshold: float = 0.7 + w_object_velocity: float = 20.0 + + +class SpotTirePush(SpotBase): + """Task getting Spot to push a tire to a goal location.""" + + name = "spot_tire_push" + config_t = SpotTirePushConfig + config: SpotTirePushConfig + + def __init__(self, config: SpotTirePushConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, config=config) + + self.body_pose_start = self.get_joint_position_start_index("base") + self.object_pose_start = self.get_joint_position_start_index("tire_joint") + self.object_vel_start = self.get_joint_velocity_start_index("tire_joint") + self.tire_y_axis_start = self.get_sensor_start_index("object_y_axis") + self.end_effector_to_object_start = self.get_sensor_start_index("sensor_arm_link_fngr") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot tire pushing task.""" + batch_size = states.shape[0] + + # (batch, horizon, size) + # or (batch, horizon) if scalar + qpos = states[..., : self.model.nq] + body_pos = qpos[..., self.body_pose_start : self.body_pose_start + 3] + object_pos = qpos[..., self.object_pose_start : self.object_pose_start + 3] + tire_y_axis = sensors[..., self.tire_y_axis_start : self.tire_y_axis_start + 3] + object_linear_velocity = states[..., self.object_vel_start : self.object_vel_start + 3] + + # Compute unit vector pointing from tire to torso + end_effector_to_object = sensors[..., self.end_effector_to_object_start : self.end_effector_to_object_start + 3] + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + end_effector_to_object, axis=-1 + ).mean(axis=-1) + + object_orientation_reward = -self.config.w_tire_orientation * np.abs( + np.dot(tire_y_axis, Z_AXIS) > self.config.orientation_threshold + ).sum(axis=-1) + + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + torso_proximity_reward = self.config.w_torso_proximity * np.linalg.norm(body_pos - object_pos, axis=-1).mean(-1) + + object_linear_velocity_penalty = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + # Check if any state in the rollout has spot fallen + body_height = qpos[..., self.body_pose_start + 2] + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Compute a penalty to prefer small commands. + controls_reward = -self.config.w_controls * np.linalg.norm(controls, axis=-1).mean(-1) + + assert object_orientation_reward.shape == (batch_size,) + assert gripper_proximity_reward.shape == (batch_size,) + assert torso_proximity_reward.shape == (batch_size,) + assert object_linear_velocity_penalty.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + + reward = ( + +spot_fallen_reward + + goal_reward + + object_orientation_reward + + torso_proximity_reward + + gripper_proximity_reward + + object_linear_velocity_penalty + + controls_reward + ) + return reward + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object.""" + # Sample object position in annulus + radius = RADIUS_MIN + (RADIUS_MAX - RADIUS_MIN) * np.random.rand() + theta = 2 * np.pi * np.random.rand() + object_pos = np.array([radius * np.cos(theta), radius * np.sin(theta)]) + 0.1 * np.random.randn(2) + + object_pose = np.array([*object_pos, TIRE_RADIUS, 1, 0, 0, 0]) + + # Place robot at random x and y + robot_pose_xy = np.random.uniform(-0.5, 0.5, 2) + random_yaw_robot = np.random.uniform(0, 2 * np.pi) + robot_pose_orientation = np.array([np.cos(random_yaw_robot / 2), 0, 0, np.sin(random_yaw_robot / 2)]) + robot_pose = np.array([*robot_pose_xy, STANDING_HEIGHT, *robot_pose_orientation]) + + return np.array([*robot_pose, *LEGS_STANDING_POS, *self.reset_arm_pos, *object_pose]) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the tire is in the goal position.""" + object_pos = data.qpos[self.object_pose_start : self.object_pose_start + 3] + object_vel = data.qvel[self.object_vel_start - self.model.nq : self.object_vel_start - self.model.nq + 3] + goal_pos = np.array(self.config.goal_position) + position_check = np.linalg.norm(object_pos - goal_pos, axis=-1, ord=np.inf) < POSITION_TOLERANCE + velocity_check = np.linalg.norm(object_vel, axis=-1) < VELOCITY_TOLERANCE + return position_check and velocity_check + + def failure(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if Spot has fallen.""" + body_height = data.qpos[self.body_pose_start + 2] + return body_height <= self.config.spot_fallen_threshold diff --git a/sumo/tasks/spot/spot_tire_rack_drag.py b/sumo/tasks/spot/spot_tire_rack_drag.py new file mode 100644 index 0000000..9116f41 --- /dev/null +++ b/sumo/tasks/spot/spot_tire_rack_drag.py @@ -0,0 +1,298 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.utils.fields import np_1d_field +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + GRIPPER_CLOSED_POS, + LEGS_STANDING_POS, + STANDING_HEIGHT, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_tire_rack.xml") + +Z_AXIS = np.array([0.0, 0.0, 1.0]) +Y_AXIS = np.array([1.0, 0.0, 0.0]) +X_AXIS = np.array([0.0, -1.0, 0.0]) # flipped for hardware + +# annulus object position sampling +RADIUS_MIN = 1.5 +RADIUS_MAX = 3.0 +USE_LEGS = False +USE_GRIPPER = True +USE_TORSO = False + +HARDWARE_FENCE_X = (-2.0, 3.0) +HARDWARE_FENCE_Y = (-3.0, 2.5) + +DEFAULT_SPOT_POS = np.array([-1.5, 0.0]) +DEFAULT_OBJECT_POS = np.array([2.0, 0.0]) + + +@dataclass +class SpotTireRackDragConfig(SpotBaseConfig): + """Config for the spot tire rack dragging task.""" + + goal_position: np.ndarray = np_1d_field( + np.array([-0.0, 0.0, 0.0], dtype=np.float64), + names=["x", "y", "z"], + mins=[-5.0, -5.0, -1.0], + maxs=[5.0, 5.0, 2.0], + steps=[0.1, 0.1, 0.1], + vis_name="tire_rack_goal_position", + xyz_vis_indices=[0, 1, 2], + xyz_vis_defaults=[-1.0, 1.3, 0.0], + ) + w_fence: float = 1000.0 + w_goal: float = 500.0 + w_orientation: float = 50.0 # Reward for keeping rack aligned with global x, y, z axes + w_heading_alignment: float = 500.0 # Reward for rack facing towards goal + object_fallen_threshold: float = 0.15 # Tire rack center of mass height + orientation_threshold: float = 0.7 + w_torso_proximity: float = 50.0 + torso_proximity_threshold: float = 1.0 + w_object_fallen: float = 2500.0 + w_gripper_to_grasp_proximity: float = 250.0 + w_gripper_orientation: float = 250.0 + w_approach_site_proximity: float = 100.0 + w_controls: float = 2.0 + w_object_velocity: float = 10.0 + # Resistance-based grasp detection parameters + resistance_threshold: float = 0.15 + fully_closed_tolerance: float = 0.05 + w_false_grasp_penalty: float = 1000.0 + w_grasp_quality: float = 100.0 + position_tolerance: float = 0.2 + + +class SpotTireRackDrag(SpotBase): + """Task getting Spot to drag a tire rack to the origin (goal location).""" + + name = "spot_tire_rack_drag" + config_t = SpotTireRackDragConfig + config: SpotTireRackDragConfig + + def __init__(self, config: SpotTireRackDragConfig | None = None) -> None: + super().__init__(model_path=XML_PATH, use_arm=True, use_gripper=True, config=config) + + self.body_pose_idx = self.get_joint_position_start_index("base") + self.object_pose_idx = self.get_joint_position_start_index("tire_rack_joint") + self.object_vel_idx = self.get_joint_velocity_start_index("tire_rack_joint") + self.object_x_axis_idx = self.get_sensor_start_index("object_x_axis") + self.object_y_axis_idx = self.get_sensor_start_index("object_y_axis") + self.object_z_axis_idx = self.get_sensor_start_index("object_z_axis") + + # Grasp point proximity sensors (left_mid only) + self.gripper_to_grasp_left_mid_idx = self.get_sensor_start_index("sensor_gripper_to_grasp_left_mid") + + # Gripper orientation sensors + self.gripper_x_axis_idx = self.get_sensor_start_index("sensor_gripper_x_axis") + self.gripper_y_axis_idx = self.get_sensor_start_index("sensor_gripper_y_axis") + self.gripper_z_axis_idx = self.get_sensor_start_index("sensor_gripper_z_axis") + + # Gripper position sensor + self.gripper_pos_idx = self.get_sensor_start_index("trace_fngr_site") + + # Torso to approach site proximity sensors (left, middle, right) + self.torso_to_approach_left_idx = self.get_sensor_start_index("sensor_torso_to_approach_left") + self.torso_to_approach_mid_idx = self.get_sensor_start_index("sensor_torso_to_approach_mid") + self.torso_to_approach_right_idx = self.get_sensor_start_index("sensor_torso_to_approach_right") + + # Get gripper joint position index for closure reward + self.gripper_joint_idx = self.get_joint_position_start_index("arm_f1x") + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the Spot tire rack dragging task.""" + batch_size = states.shape[0] + + qpos = states[..., : self.model.nq] + + body_height = qpos[..., self.body_pose_idx + 2] + body_pos = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + object_pos = qpos[..., self.object_pose_idx : self.object_pose_idx + 3] + object_linear_velocity = states[..., self.object_vel_idx : self.object_vel_idx + 3] + + object_z_axis = sensors[..., self.object_z_axis_idx : self.object_z_axis_idx + 3] + object_x_axis = sensors[..., self.object_x_axis_idx : self.object_x_axis_idx + 3] + object_y_axis = sensors[..., self.object_y_axis_idx : self.object_y_axis_idx + 3] + + # Grasp point proximity vector + gripper_to_grasp_left_mid = sensors[ + ..., self.gripper_to_grasp_left_mid_idx : self.gripper_to_grasp_left_mid_idx + 3 + ] + + # Gripper orientation axes + gripper_x_axis = sensors[..., self.gripper_x_axis_idx : self.gripper_x_axis_idx + 3] + gripper_y_axis = sensors[..., self.gripper_y_axis_idx : self.gripper_y_axis_idx + 3] + gripper_z_axis = sensors[..., self.gripper_z_axis_idx : self.gripper_z_axis_idx + 3] + + # Gripper position + gripper_pos = sensors[..., self.gripper_pos_idx : self.gripper_pos_idx + 3] + + # Torso to approach site vectors + torso_to_approach_left = sensors[..., self.torso_to_approach_left_idx : self.torso_to_approach_left_idx + 3] + torso_to_approach_mid = sensors[..., self.torso_to_approach_mid_idx : self.torso_to_approach_mid_idx + 3] + torso_to_approach_right = sensors[..., self.torso_to_approach_right_idx : self.torso_to_approach_right_idx + 3] + + # === Resistance-Based Grasp Detection === + gripper_joint_pos = qpos[..., self.gripper_joint_idx] + gripper_joint_cmd = controls[..., 9] # ARM_CMD_INDS[6] maps to controls index 9 + + # Position error: actual - commanded + position_error = gripper_joint_pos - gripper_joint_cmd + + # Grasp detection logic + has_resistance = position_error > self.config.resistance_threshold + is_fully_closed = np.abs(gripper_joint_pos - GRIPPER_CLOSED_POS) < self.config.fully_closed_tolerance + is_not_empty = ~is_fully_closed + is_grasping = has_resistance & is_not_empty + is_false_grasp = is_fully_closed & (gripper_joint_cmd < -0.5) + + # Fence reward + fence_violated_x = (body_pos[..., 0] < HARDWARE_FENCE_X[0]) | (body_pos[..., 0] > HARDWARE_FENCE_X[1]) + fence_violated_y = (body_pos[..., 1] < HARDWARE_FENCE_Y[0]) | (body_pos[..., 1] > HARDWARE_FENCE_Y[1]) + spot_fence_reward = -self.config.w_fence * (fence_violated_x | fence_violated_y).any(axis=-1) + + # Check if spot has fallen + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Check if object has fallen + object_fallen_reward = -self.config.w_object_fallen * ( + object_pos[..., 2] <= self.config.object_fallen_threshold + ).any(axis=-1) + + # Goal reward - distance to goal position + goal_reward = -self.config.w_goal * np.linalg.norm( + object_pos - np.array(self.config.goal_position)[None, None], axis=-1 + ).mean(-1) + + # Orientation reward - keep rack aligned with global x, y, z axes + x_alignment = np.sum(object_x_axis * X_AXIS, axis=-1) + y_alignment = np.sum(object_y_axis * Y_AXIS, axis=-1) + z_alignment = np.sum(object_z_axis * Z_AXIS, axis=-1) + orientation_error = (1.0 - x_alignment) + (1.0 - y_alignment) + (1.0 - z_alignment) + object_orientation_reward = -self.config.w_orientation * orientation_error.mean(axis=-1) + + # Heading alignment reward - keep rack's x-axis pointing towards goal + rack_to_goal = np.array(self.config.goal_position)[None, None, :2] - object_pos[..., :2] + rack_to_goal_norm = rack_to_goal / (np.linalg.norm(rack_to_goal, axis=-1, keepdims=True) + 1e-8) + + object_x_horizontal = object_x_axis[..., :2] + object_x_horizontal_norm = object_x_horizontal / ( + np.linalg.norm(object_x_horizontal, axis=-1, keepdims=True) + 1e-8 + ) + + heading_alignment = np.sum(object_x_horizontal_norm * rack_to_goal_norm, axis=-1) + heading_alignment_abs = np.abs(heading_alignment) + heading_error = 1.0 - heading_alignment_abs + heading_alignment_reward = -self.config.w_heading_alignment * heading_error.mean(axis=-1) + + # Approach site proximity reward + distance_to_left_approach = np.linalg.norm(torso_to_approach_left, axis=-1) + distance_to_mid_approach = np.linalg.norm(torso_to_approach_mid, axis=-1) + distance_to_right_approach = np.linalg.norm(torso_to_approach_right, axis=-1) + approach_distances = np.stack( + [distance_to_left_approach, distance_to_mid_approach, distance_to_right_approach], axis=-1 + ) + min_approach_distance = np.min(approach_distances, axis=-1) + approach_site_proximity_reward = -self.config.w_approach_site_proximity * min_approach_distance.mean(-1) + + # Torso proximity reward + torso_proximity_reward = self.config.w_torso_proximity * np.minimum( + self.config.torso_proximity_threshold, np.linalg.norm(body_pos - object_pos, axis=-1) + ).mean(-1) + + # Gripper to grasp point proximity + distance_to_left_mid = np.linalg.norm(gripper_to_grasp_left_mid, axis=-1) + gripper_to_grasp_proximity_reward = -self.config.w_gripper_to_grasp_proximity * distance_to_left_mid.mean(-1) + + # Gripper orientation reward - only require gripper z-axis to align with object z-axis (both upright) + gripper_z_axis_norm = gripper_z_axis / (np.linalg.norm(gripper_z_axis, axis=-1, keepdims=True) + 1e-8) + object_z_axis_norm = object_z_axis / (np.linalg.norm(object_z_axis, axis=-1, keepdims=True) + 1e-8) + z_dot_product = np.sum(gripper_z_axis_norm * object_z_axis_norm, axis=-1) + z_alignment_error = 1.0 - z_dot_product + gripper_orientation_reward = -self.config.w_gripper_orientation * z_alignment_error.mean(axis=-1) + + # Object velocity penalty + object_linear_velocity_reward = -self.config.w_object_velocity * np.square( + np.linalg.norm(object_linear_velocity, axis=-1).mean(-1) + ) + + # Control penalty + controls_reward = -self.config.w_controls * np.linalg.norm(controls[..., :3], axis=-1).mean(-1) + + # Grasp-based rewards + false_grasp_penalty = -self.config.w_false_grasp_penalty * is_false_grasp.any(axis=-1) + grasp_quality = np.clip(position_error / 0.5, 0, 1) + grasp_quality_reward = self.config.w_grasp_quality * (is_grasping * grasp_quality).mean(axis=-1) + + assert spot_fence_reward.shape == (batch_size,) + assert spot_fallen_reward.shape == (batch_size,) + assert object_fallen_reward.shape == (batch_size,) + assert goal_reward.shape == (batch_size,) + assert object_orientation_reward.shape == (batch_size,) + assert heading_alignment_reward.shape == (batch_size,) + assert approach_site_proximity_reward.shape == (batch_size,) + assert torso_proximity_reward.shape == (batch_size,) + assert gripper_to_grasp_proximity_reward.shape == (batch_size,) + assert gripper_orientation_reward.shape == (batch_size,) + assert object_linear_velocity_reward.shape == (batch_size,) + assert controls_reward.shape == (batch_size,) + assert false_grasp_penalty.shape == (batch_size,) + assert grasp_quality_reward.shape == (batch_size,) + + return ( + spot_fence_reward + + spot_fallen_reward + + object_fallen_reward + + goal_reward + + object_orientation_reward + + approach_site_proximity_reward + + gripper_to_grasp_proximity_reward + + gripper_orientation_reward + + false_grasp_penalty + + grasp_quality_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose of robot and object - tire rack starts upright.""" + # Tire rack starts upright (no rotation) at elevated position (z=0.3) + reset_object_pose = np.array([*DEFAULT_OBJECT_POS, 0.3, 1, 0, 0, 0]) + + return np.array( + [ + *DEFAULT_SPOT_POS, + STANDING_HEIGHT, + 1, + 0, + 0, + 0, + *LEGS_STANDING_POS, + *self.reset_arm_pos, + *reset_object_pose, + ] + ) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the tire rack has been successfully dragged to the goal position.""" + object_pos = data.qpos[self.object_pose_idx : self.object_pose_idx + 3] + goal_position = np.array(self.config.goal_position) + + # Check position tolerance + distance_to_goal = np.linalg.norm(object_pos - goal_position) + position_success = distance_to_goal <= self.config.position_tolerance + + return bool(position_success) diff --git a/sumo/tasks/spot/spot_tire_roll.py b/sumo/tasks/spot/spot_tire_roll.py new file mode 100644 index 0000000..053cd12 --- /dev/null +++ b/sumo/tasks/spot/spot_tire_roll.py @@ -0,0 +1,13 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.tasks.spot.spot_tire_roll import SpotTireRoll as _JudoSpotTireRoll +from judo.tasks.spot.spot_tire_roll import SpotTireRollConfig + +from sumo.tasks.spot.spot_base import SpotAssetMixin + + +class SpotTireRoll(SpotAssetMixin, _JudoSpotTireRoll): + """Public judo-rai SpotTireRoll with Sumo asset resolution.""" + + +__all__ = ["SpotTireRoll", "SpotTireRollConfig"] diff --git a/sumo/tasks/spot/spot_tire_stack.py b/sumo/tasks/spot/spot_tire_stack.py new file mode 100644 index 0000000..9736778 --- /dev/null +++ b/sumo/tasks/spot/spot_tire_stack.py @@ -0,0 +1,214 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""SpotTireStack task - stack one tire on another. + +Adapted from judo/tasks/spot/spot_tire_stack.py. +""" + +from dataclasses import dataclass +from typing import Any + +import numpy as np +from judo.tasks.spot.spot_utils import apply_quat_to_vec +from mujoco import MjData, MjModel + +from sumo import MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_constants import ( + LEGS_STANDING_POS, + STANDING_HEIGHT, + TIRE_HALF_WIDTH, + TIRE_RADIUS, +) + +XML_PATH = str(MODEL_PATH / "xml/spot_tasks/spot_two_tires.xml") + + +@dataclass +class SpotTireStackConfig(SpotBaseConfig): + """Configuration for the SpotTireStack task.""" + + w_tire_stack_xy: float = 100.0 + w_tire_stack_z: float = 200.0 + w_bottom_tire_velocity: float = 50.0 + w_gripper_proximity: float = 10.0 + w_torso_proximity: float = 10.0 + w_tire_orientation: float = 20.0 + fall_penalty: float = 10_000.0 + w_controls: float = 1.0 + w_gripper_height_penalty: float = 1000.0 + orientation_reward_distance_threshold: float = 2.0 + + # Success criteria (sumo-specific) + stack_xy_tolerance: float = 0.1 + stack_z_tolerance: float = 0.1 + + +class SpotTireStack(SpotBase[SpotTireStackConfig]): + """Task getting Spot to stack one tire on top of another.""" + + name: str = "spot_tire_stack" + config_t: type[SpotTireStackConfig] = SpotTireStackConfig # type: ignore[assignment] + config: SpotTireStackConfig + + def __init__( + self, + config: SpotTireStackConfig | None = None, + ) -> None: + """Initialize the SpotTireStack task.""" + super().__init__(model_path=XML_PATH, use_arm=True, use_torso=False, config=config) + self.body_pose_idx = self.get_joint_position_start_index("base") + self.bottom_tire_pose_idx = self.get_joint_position_start_index("tire_rubber_joint") + self.top_tire_pose_idx = self.get_joint_position_start_index("tire_rubber_2_joint") + self.gripper_pos_idx = self.get_sensor_start_index("trace_fngr_site") + self.top_tire_y_axis_idx = self.get_sensor_start_index("top_tire_y_axis") + + # Velocity indices + self.bottom_tire_vel_idx = self.model.jnt_dofadr[self.model.joint("tire_rubber_joint").id] + self.top_tire_vel_idx = self.model.jnt_dofadr[self.model.joint("tire_rubber_2_joint").id] + + def reward( + self, + states: np.ndarray, + sensors: np.ndarray, + controls: np.ndarray, + system_metadata: dict[str, Any] | None = None, + ) -> np.ndarray: + """Reward function for the tire stack task.""" + batch_size = states.shape[0] + qpos = states[..., : self.model.nq] + qvel = states[..., self.model.nq :] + + p_W_bottom_tire = qpos[..., self.bottom_tire_pose_idx : self.bottom_tire_pose_idx + 3] + p_W_top_tire = qpos[..., self.top_tire_pose_idx : self.top_tire_pose_idx + 3] + p_WB = qpos[..., self.body_pose_idx : self.body_pose_idx + 3] + p_WG = sensors[..., self.gripper_pos_idx : self.gripper_pos_idx + 3] + + # XY alignment + p_bottom_to_top_xy = p_W_top_tire[..., :2] - p_W_bottom_tire[..., :2] + xy_distance = np.linalg.norm(p_bottom_to_top_xy, axis=-1) + tire_stack_xy_reward = -self.config.w_tire_stack_xy * xy_distance.mean(axis=-1) + + # Z stacking + desired_top_tire_z = p_W_bottom_tire[..., 2] + TIRE_HALF_WIDTH * 2 + z_error = np.abs(p_W_top_tire[..., 2] - desired_top_tire_z) + tire_stack_z_reward = -self.config.w_tire_stack_z * z_error.mean(axis=-1) + + # Tire orientation + tire_distance = np.linalg.norm(p_W_top_tire - p_W_bottom_tire, axis=-1) + orientation_active = tire_distance < self.config.orientation_reward_distance_threshold + top_tire_y_axis = sensors[..., self.top_tire_y_axis_idx : self.top_tire_y_axis_idx + 3] + top_to_bottom_vec = p_W_bottom_tire - p_W_top_tire + top_to_bottom_norm = np.maximum(np.linalg.norm(top_to_bottom_vec, axis=-1, keepdims=True), 1e-6) + top_to_bottom_unit = top_to_bottom_vec / top_to_bottom_norm + dot_product = np.sum(top_tire_y_axis * top_to_bottom_unit, axis=-1) + orientation_error = 1.0 - dot_product + orientation_active_any = orientation_active.any(axis=-1) + tire_orientation_reward = np.where( + orientation_active_any, -self.config.w_tire_orientation * orientation_error.mean(axis=-1), 0.0 + ) + + # Bottom tire velocity + bottom_tire_lin_vel = qvel[..., self.bottom_tire_vel_idx : self.bottom_tire_vel_idx + 3] + bottom_tire_ang_vel = qvel[..., self.bottom_tire_vel_idx + 3 : self.bottom_tire_vel_idx + 6] + bottom_tire_velocity_reward = -self.config.w_bottom_tire_velocity * ( + np.linalg.norm(bottom_tire_lin_vel, axis=-1).mean(axis=-1) + + np.linalg.norm(bottom_tire_ang_vel, axis=-1).mean(axis=-1) + ) + + # Torso shaping + p_bottom_to_top = p_W_top_tire - p_W_bottom_tire + p_bottom_to_top_norm = np.maximum(np.linalg.norm(p_bottom_to_top, axis=-1, keepdims=True), 1e-6) + u_stacking_direction = p_bottom_to_top / p_bottom_to_top_norm + + max_distance = 3.0 * TIRE_RADIUS + min_distance = 1.0 * TIRE_RADIUS + normalized_distance = np.clip((tire_distance - min_distance) / (max_distance - min_distance), 0, 1) + angle_degrees = -90.0 * (1.0 - normalized_distance) - 25.0 + angle_radians = np.deg2rad(angle_degrees) + cos_half_angle = np.cos(angle_radians / 2) + sin_half_angle = np.sin(angle_radians / 2) + rotation_quat = np.stack( + [cos_half_angle, np.zeros_like(cos_half_angle), np.zeros_like(cos_half_angle), sin_half_angle], axis=-1 + ) + u_stacking_direction_rotated = apply_quat_to_vec(rotation_quat, u_stacking_direction) + + distance_from_tire = 0.8 + p_WB_desired = p_W_top_tire + distance_from_tire * u_stacking_direction_rotated + p_WB_desired[..., 2] = STANDING_HEIGHT + + torso_proximity_reward = -self.config.w_torso_proximity * np.linalg.norm(p_WB - p_WB_desired, axis=-1).mean( + axis=-1 + ) + + # Fall penalty + body_height = qpos[..., self.body_pose_idx + 2] + spot_fallen_reward = -self.config.fall_penalty * (body_height <= self.config.spot_fallen_threshold).any(axis=-1) + + # Gripper shaping + z_world = np.zeros_like(p_W_top_tire) + z_world[..., 2] = 1.0 + Ty_W = sensors[..., self.top_tire_y_axis_idx : self.top_tire_y_axis_idx + 3] + z_proj_perp = z_world - np.sum(z_world * Ty_W, axis=-1, keepdims=True) * Ty_W + z_proj_perp_norm = np.maximum(np.linalg.norm(z_proj_perp, axis=-1, keepdims=True), 1e-6) + u_top = z_proj_perp / z_proj_perp_norm + p_W_gripper_des = p_W_top_tire + TIRE_RADIUS * u_top + gripper_proximity_reward = -self.config.w_gripper_proximity * np.linalg.norm( + p_WG - p_W_gripper_des, axis=-1 + ).mean(axis=-1) + + # Gripper height penalty + max_tire_height = 2 * TIRE_RADIUS + gripper_height = p_WG[..., 2] + gripper_height_excess = np.maximum(0, gripper_height - max_tire_height) + gripper_height_penalty = -self.config.w_gripper_height_penalty * gripper_height_excess.mean(axis=-1) + + controls_reward = -self.config.w_controls * np.linalg.norm(controls, axis=-1).mean(-1) + + assert tire_stack_xy_reward.shape == (batch_size,) + return ( + tire_stack_xy_reward + + tire_stack_z_reward + + tire_orientation_reward + + bottom_tire_velocity_reward + + gripper_proximity_reward + + torso_proximity_reward + + spot_fallen_reward + + gripper_height_penalty + + controls_reward + ) + + @property + def reset_pose(self) -> np.ndarray: + """Reset pose for the tire stack task.""" + robot_pose = np.array([-2.0, -2.0, STANDING_HEIGHT, 1.0, 0.0, 0.0, 0.0]) + + # Bottom tire - lying flat + tire_1_pose = np.zeros(7) + tire_1_pose[0] = -2.0 + tire_1_pose[1] = 1.0 + tire_1_pose[2] = TIRE_HALF_WIDTH + tire_1_pose[3:] = [1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0] + + # Top tire - upright + tire_2_pose = np.zeros(7) + tire_2_pose[0] = -2.0 + tire_2_pose[1] = 0.0 + tire_2_pose[2] = TIRE_RADIUS + tire_2_pose[3:] = [1.0, 0.0, 0.0, 0.0] + + return np.array([*robot_pose, *LEGS_STANDING_POS, *self.reset_arm_pos, *tire_1_pose, *tire_2_pose]) + + def success(self, model: MjModel, data: MjData, metadata: dict[str, Any] | None = None) -> bool: + """Check if the tires are successfully stacked.""" + bottom_tire_pos = data.qpos[self.bottom_tire_pose_idx : self.bottom_tire_pose_idx + 3] + top_tire_pos = data.qpos[self.top_tire_pose_idx : self.top_tire_pose_idx + 3] + + xy_distance = np.linalg.norm(top_tire_pos[:2] - bottom_tire_pos[:2]) + xy_aligned = xy_distance <= self.config.stack_xy_tolerance + + desired_top_tire_z = bottom_tire_pos[2] + TIRE_HALF_WIDTH * 2 + z_error = np.abs(top_tire_pos[2] - desired_top_tire_z) + z_correct = z_error <= self.config.stack_z_tolerance + + return bool(xy_aligned and z_correct) diff --git a/sumo/tasks/spot/spot_tire_upright.py b/sumo/tasks/spot/spot_tire_upright.py new file mode 100644 index 0000000..4bef9c7 --- /dev/null +++ b/sumo/tasks/spot/spot_tire_upright.py @@ -0,0 +1,13 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from judo.tasks.spot.spot_tire_upright import SpotTireUpright as _JudoSpotTireUpright +from judo.tasks.spot.spot_tire_upright import SpotTireUprightConfig + +from sumo.tasks.spot.spot_base import SpotAssetMixin + + +class SpotTireUpright(SpotAssetMixin, _JudoSpotTireUpright): + """Public judo-rai SpotTireUpright with Sumo asset resolution.""" + + +__all__ = ["SpotTireUpright", "SpotTireUprightConfig"] diff --git a/sumo/utils/__init__.py b/sumo/utils/__init__.py new file mode 100644 index 0000000..536dbd9 --- /dev/null +++ b/sumo/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. diff --git a/sumo/utils/extensions.py b/sumo/utils/extensions.py new file mode 100644 index 0000000..a62daa7 --- /dev/null +++ b/sumo/utils/extensions.py @@ -0,0 +1,32 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from importlib import import_module +from types import ModuleType + + +def require_g1_extensions() -> ModuleType: + """Import the local G1 native extension with a repo-specific error.""" + try: + return import_module("g1_extensions") + except ImportError as exc: + raise ImportError( + "G1 tasks require the local `g1_extensions` package.\n" + "Install the dev environment and build native extensions with:\n" + " pixi install -e dev\n" + " pixi run build" + ) from exc + + +def require_mujoco_extensions() -> ModuleType: + """Import Judo's MuJoCo policy extension with a repo-specific error.""" + try: + return import_module("mujoco_extensions.policy_rollout") + except ImportError as exc: + raise ImportError( + "Spot tasks require Judo's `mujoco_extensions` package.\n" + "Install the dev environment and build native extensions with:\n" + " pixi install -e dev\n" + " pixi run build-judo-ext\n" + "For the full sumo app, G1 tasks also require:\n" + " pixi run build" + ) from exc diff --git a/sumo/utils/indexing.py b/sumo/utils/indexing.py new file mode 100644 index 0000000..4e0ec07 --- /dev/null +++ b/sumo/utils/indexing.py @@ -0,0 +1,257 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +from typing import Tuple + +import mujoco +import numpy as np + + +def get_joint_dof(joint_type: mujoco.mjtJoint) -> Tuple[int, int]: + """Get the number of position and velocity coordinates for a joint type. + + Args: + joint_type: MuJoCo joint type (mjJNT_FREE, mjJNT_BALL, mjJNT_SLIDE, mjJNT_HINGE) + + Returns: + Tuple containing: + - Number of position coordinates (qpos) + - Number of velocity coordinates (qvel) + + Notes: + Joint types and their DOFs: + - free: 7 qpos (x,y,z, qw,qx,qy,qz), 6 qvel (vx,vy,vz, wx,wy,wz) + - ball: 4 qpos (qw,qx,qy,qz), 3 qvel (wx,wy,wz) + - slide: 1 qpos (translation), 1 qvel (linear velocity) + - hinge: 1 qpos (rotation), 1 qvel (angular velocity) + """ + if joint_type == mujoco.mjtJoint.mjJNT_FREE: + return 7, 6 # position: 3 translation + 4 quaternion, velocity: 3 linear + 3 angular + elif joint_type == mujoco.mjtJoint.mjJNT_BALL: + return 4, 3 # position: 4 quaternion, velocity: 3 angular + elif joint_type in [mujoco.mjtJoint.mjJNT_SLIDE, mujoco.mjtJoint.mjJNT_HINGE]: + return 1, 1 # position: 1 coordinate, velocity: 1 rate + else: + raise ValueError(f"Unknown joint type: {joint_type}") + + +def get_control_indices(model: mujoco.MjModel, joint_name: str | list[str]) -> np.ndarray: + """Get the indices in data.ctrl for the actions. + + Args: + model: MuJoCo model + joint_name: Name of the joint or list of joint names to find control indices for + + Returns: + List of indices in data.ctrl corresponding to the joint's actuators + + Raises: + ValueError: If joint name is not found in model + """ + if isinstance(joint_name, list): + return np.concatenate([get_control_indices(model, name) for name in joint_name]) + elif isinstance(joint_name, str): + joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, joint_name) + if joint_id == -1: + raise ValueError(f"Could not find joint named '{joint_name}' in model") + + # Find actuators that control this joint + actuator_indices = [] + for i in range(model.nu): + if model.actuator_trnid[i, 0] == joint_id: + actuator_indices.append(i) + + return np.array(actuator_indices) + else: + raise ValueError(f"Invalid joint name type: provided {type(joint_name)}") + + +def get_vel_indices(model: mujoco.MjModel, joint_name: str | list[str]) -> np.ndarray: + """Get velocity indices for any joint type. + + Args: + model: MuJoCo model + joint_name: Name of the joint or list of joint names to find velocity indices for + + Returns: + Tuple containing: + - List of velocity indices (qvel) + """ + if isinstance(joint_name, list): + return np.concatenate([get_vel_indices(model, name) for name in joint_name]) + elif isinstance(joint_name, str): + joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, joint_name) + if joint_id == -1: + raise ValueError(f"Could not find joint named '{joint_name}' in model") + + # Get the starting indices for this joint's DOFs + qvel_start_idx = model.jnt_dofadr[joint_id] + + # Get the number of position and velocity coordinates for this joint type + joint_type = model.jnt_type[joint_id] + _, nqvel = get_joint_dof(joint_type) + + qvel_indices = np.array(range(qvel_start_idx, qvel_start_idx + nqvel)) + + return qvel_indices + else: + raise ValueError(f"Invalid joint name type: provided {type(joint_name)}") + + +def get_pos_indices(model: mujoco.MjModel, joint_name: str | list[str]) -> np.ndarray: + """Get position indices for any joint type. + + Args: + model: MuJoCo model + joint_name: Name of the joint or list of joint names to find position indices for + + Returns: + Tuple containing: + - List of position indices (qpos) + """ + if isinstance(joint_name, list): + return np.concatenate([get_pos_indices(model, name) for name in joint_name]) + elif isinstance(joint_name, str): + joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, joint_name) + if joint_id == -1: + raise ValueError(f"Could not find joint named '{joint_name}' in model.") + + # Get the starting indices for this joint's DOFs + qpos_start_idx = model.jnt_qposadr[joint_id] + + # Get the number of position and velocity coordinates for this joint type + joint_type = model.jnt_type[joint_id] + nqpos, _ = get_joint_dof(joint_type) + + qpos_indices = np.array(range(qpos_start_idx, qpos_start_idx + nqpos)) + + return qpos_indices + else: + raise ValueError(f"Invalid joint name type: provided {type(joint_name)}") + + +def get_state_indices(model: mujoco.MjModel, joint_name: str | list[str]) -> Tuple[np.ndarray, np.ndarray]: + """Get position and velocity indices for any joint type. + + Args: + model: MuJoCo model + joint_name: Name of the joint or list of joint names to find position and velocity indices for + + Returns: + Tuple containing: + - List of position indices (qpos) + - List of velocity indices (qvel) + """ + qpos_indices = get_pos_indices(model, joint_name) + qvel_indices = get_vel_indices(model, joint_name) + + return qpos_indices, qvel_indices + + +def get_sensor_indices(model: mujoco.MjModel, sensor_name: str | list[str]) -> np.ndarray: + """Get the indices in data.sensordata for a given sensor name. + + Args: + model: MuJoCo model + sensor_name: Name of the sensor or list of sensor names to find indices for + + Returns: + List of indices in data.sensordata corresponding to the sensor + + Raises: + ValueError: If sensor name is not found in model + """ + if isinstance(sensor_name, list): + return np.concatenate([get_sensor_indices(model, name) for name in sensor_name]) + elif isinstance(sensor_name, str): + sensor_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, sensor_name) + if sensor_id == -1: + raise ValueError(f"Could not find sensor named '{sensor_name}' in model") + + # Get the starting index and dimension for this sensor's data + adr = model.sensor_adr[sensor_id] + dim = model.sensor_dim[sensor_id] + + return np.array(range(adr, adr + dim)) + else: + raise ValueError(f"Invalid sensor name type: provided {type(sensor_name)}") + + +def get_control_range(model: mujoco.MjModel, joint_name: str | list[str]) -> np.ndarray: + """Get the control range of a joint. + + Args: + model: MuJoCo model + joint_name: Name of the joint or list of joint names to find control range for + + Returns: + Control range of the joint + """ + if isinstance(joint_name, list): + return np.array([get_control_range(model, name) for name in joint_name]) + elif isinstance(joint_name, str): + joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, joint_name) + if joint_id == -1: + raise ValueError(f"Joint '{joint_name}' not found in the model.") + + # Find the actuator controlling this joint + for i in range(model.nu): + if model.actuator_trnid[i, 0] == joint_id: # First column is joint ID + return model.actuator_ctrlrange[i] + + raise ValueError(f"No actuator found for joint '{joint_name}'.") + else: + raise ValueError(f"Invalid joint name type: provided {type(joint_name)}") + + +def get_joint_names(model: mujoco.MjModel) -> list[str]: + """Returns a list of joint names from the model. + + Args: + model: MuJoCo model + + Returns: + List of joint names + """ + return [mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_JOINT, i) for i in range(model.njnt)] + + +def get_joint_type(model: mujoco.MjModel, joint_name: str) -> mujoco.mjtJoint: + """Returns the type of a joint. + + Args: + model: MuJoCo model + joint_name: Name of the joint + + Returns: + Type of the joint + """ + return model.jnt_type[mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, joint_name)] + + +def get_joint_pos_index_map(model: mujoco.MjModel) -> dict[str, list[int]]: + """Get all the position indices from the model. + + Args: + model: MuJoCo model + + Returns: + Map of position indices + """ + return { + joint_name: get_pos_indices(model, joint_name).astype(int).tolist() for joint_name in get_joint_names(model) + } + + +def get_joint_vel_index_map(model: mujoco.MjModel) -> dict[str, list[int]]: + """Get all the velocity indices from the model. + + Args: + model: MuJoCo model + + Returns: + Map of velocity indices + """ + return { + f"{joint_name}_vel": (model.nq + get_vel_indices(model, joint_name)).astype(int).tolist() + for joint_name in get_joint_names(model) + } diff --git a/sumo/utils/mujoco.py b/sumo/utils/mujoco.py new file mode 100644 index 0000000..c700d43 --- /dev/null +++ b/sumo/utils/mujoco.py @@ -0,0 +1,49 @@ +# Copyright (c) 2025-2026 Robotics and AI Institute LLC dba RAI Institute. All rights reserved. + +"""G1 rollout backend using sumo's local C++ extensions.""" + +import numpy as np +from judo.utils.mj_rollout_backend import make_model_data_pairs +from judo.utils.rollout_backend import RolloutBackend +from mujoco import MjModel + +from sumo.utils.extensions import require_g1_extensions + +# Re-export for convenience +__all__ = ["G1RolloutBackend", "make_model_data_pairs"] + + +class G1RolloutBackend(RolloutBackend): + """Rollout backend for G1 tasks using C++ G1Rollout extension.""" + + def __init__( + self, + model: MjModel, + num_threads: int, + cutoff_time: float = 0.2, + ) -> None: + self.model = model + self.num_threads = num_threads + self.cutoff_time = cutoff_time + g1_extensions = require_g1_extensions() + self._rollout_obj = g1_extensions.G1Rollout(nthread=num_threads, cutoff_time=cutoff_time) + self._models, self._datas = make_model_data_pairs(model, num_threads) + + def rollout( + self, + x0: np.ndarray, + controls: np.ndarray, + last_policy_output: np.ndarray | None = None, + ) -> tuple[np.ndarray, np.ndarray, None]: + """Conduct parallel rollouts using the G1 C++ backend.""" + x0_batched = np.tile(x0, (len(self._models), 1)) + out_states, out_sensors = self._rollout_obj.rollout(self._models, self._datas, x0_batched, controls) + return np.array(out_states), np.array(out_sensors), None + + def update(self, num_threads: int) -> None: + """Update the number of threads.""" + self.num_threads = num_threads + self._rollout_obj.close() + g1_extensions = require_g1_extensions() + self._rollout_obj = g1_extensions.G1Rollout(nthread=num_threads, cutoff_time=self.cutoff_time) + self._models, self._datas = make_model_data_pairs(self.model, num_threads) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..786dfba --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,34 @@ +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).resolve().parents[1] +PRIVATE_JUDO_ROOT = REPO_ROOT / "judo-private" + + +@pytest.fixture(scope="session", autouse=True) +def bootstrap_public_judo_assets() -> None: + import judo + + judo_path = Path(judo.__file__).resolve() + if PRIVATE_JUDO_ROOT in judo_path.parents: + raise RuntimeError(f"Expected public judo-rai, got local private checkout at {judo_path}") + + mesh_root = judo.MODEL_PATH / "meshes" + if not (mesh_root / "spot").exists() or not (mesh_root / "objects" / "tire").exists(): + from judo.utils.assets import download_and_extract_meshes + + download_and_extract_meshes( + extract_root=str(judo.MODEL_PATH), repo="bdaiinstitute/judo", asset_name="meshes.zip" + ) + + +def pytest_collection_modifyitems(config, items): + """Skip tests that require g1_extensions if it's not available.""" + try: + import g1_extensions # noqa: F401 + except ImportError: + skip_cpp = pytest.mark.skip(reason="g1_extensions not built") + for item in items: + if "g1_extensions" in item.keywords: + item.add_marker(skip_cpp) diff --git a/tests/test_imports.py b/tests/test_imports.py new file mode 100644 index 0000000..d772cb5 --- /dev/null +++ b/tests/test_imports.py @@ -0,0 +1,170 @@ +import importlib +import os +from pathlib import Path + +import pytest + +SPOT_TASK_MODULES = [ + "sumo.tasks.spot.spot_base", + "sumo.tasks.spot.spot_box_push", + "sumo.tasks.spot.spot_chair_push", + "sumo.tasks.spot.spot_cone_push", + "sumo.tasks.spot.spot_rack_push", + "sumo.tasks.spot.spot_tire_push", + "sumo.tasks.spot.spot_cone_upright", + "sumo.tasks.spot.spot_chair_ramp", + "sumo.tasks.spot.spot_barrier_upright", + "sumo.tasks.spot.spot_barrier_drag", + "sumo.tasks.spot.spot_tire_roll", + "sumo.tasks.spot.spot_tire_upright", + "sumo.tasks.spot.spot_tire_stack", + "sumo.tasks.spot.spot_tire_rack_drag", + "sumo.tasks.spot.spot_rugged_box_push", +] + +G1_TASK_NAMES = [ + "g1_base", + "g1_box", + "g1_chair_push", + "g1_door", + "g1_table_push", +] + +SPOT_TASK_NAMES = [ + "spot_base", + "spot_box_push", + "spot_chair_push", + "spot_cone_push", + "spot_rack_push", + "spot_tire_push", + "spot_cone_upright", + "spot_chair_ramp", + "spot_barrier_upright", + "spot_barrier_drag", + "spot_tire_roll", + "spot_tire_upright", + "spot_tire_stack", + "spot_tire_rack_drag", + "spot_rugged_box_push", +] + +G1_IMPORTS = { + "sumo.tasks.g1.g1_base": ["G1Base", "G1BaseConfig"], + "sumo.tasks.g1.g1_box": ["G1Box", "G1BoxConfig"], + "sumo.tasks.g1.g1_chair_push": ["G1ChairPush", "G1ChairPushConfig"], + "sumo.tasks.g1.g1_door": ["G1Door", "G1DoorConfig"], + "sumo.tasks.g1.g1_table_push": ["G1TablePush", "G1TablePushConfig"], +} + + +def test_public_judo_resolution(): + import judo + + judo_path = Path(judo.__file__).resolve() + assert "judo-private" not in str(judo_path) + + +def test_sumo_package(): + import sumo + + assert hasattr(sumo, "PACKAGE_ROOT") + assert hasattr(sumo, "MODEL_PATH") + assert sumo.MODEL_PATH.exists() + + +def test_sumo_utils(): + from sumo.utils import indexing, mujoco + + assert hasattr(indexing, "get_pos_indices") + assert hasattr(indexing, "get_sensor_indices") + assert hasattr(indexing, "get_vel_indices") + assert hasattr(mujoco, "G1RolloutBackend") + assert hasattr(mujoco, "make_model_data_pairs") + + +def test_sumo_tasks_import(): + assert importlib.import_module("sumo.tasks") is not None + + for module_name, attrs in G1_IMPORTS.items(): + module = importlib.import_module(module_name) + for attr in attrs: + assert hasattr(module, attr) + + +@pytest.mark.parametrize("module_name", SPOT_TASK_MODULES) +def test_spot_task_module_imports(module_name): + assert importlib.import_module(module_name) is not None + + +def test_tasks_registered(): + from judo.tasks import get_registered_tasks + + importlib.import_module("sumo.tasks") + tasks = get_registered_tasks() + for name in G1_TASK_NAMES + SPOT_TASK_NAMES: + assert name in tasks, f"Task '{name}' not found in registry" + + +def test_sumo_controller_import(): + module = importlib.import_module("sumo.controller") + assert hasattr(module, "Controller") + assert hasattr(module, "ControllerConfig") + + +def test_sumo_run_mpc_import(): + module = importlib.import_module("sumo.run_mpc.run_mpc") + assert hasattr(module, "run_mpc") + assert hasattr(module, "RunMPCConfig") + + +def test_sumo_dora_imports(): + from sumo.app.dora.controller import ControllerNode + from sumo.app.dora.simulation import G1Simulation, SimulationNode + + assert ControllerNode is not None + assert SimulationNode is not None + assert G1Simulation is not None + + +def test_prefer_active_env_libs(monkeypatch, tmp_path): + import sumo + + prefix = tmp_path / "pixi-env" + lib_dir = prefix / "lib" + lib_dir.mkdir(parents=True) + + monkeypatch.setenv("CONDA_PREFIX", str(prefix)) + monkeypatch.setenv("LD_LIBRARY_PATH", "/existing/lib") + monkeypatch.delenv("DYLD_FALLBACK_LIBRARY_PATH", raising=False) + + sumo._prefer_active_env_libs() + assert os.environ["LD_LIBRARY_PATH"].split(os.pathsep)[0] == str(lib_dir) + + sumo._prefer_active_env_libs() + assert os.environ["LD_LIBRARY_PATH"].split(os.pathsep).count(str(lib_dir)) == 1 + + +def test_simulation_backend_resolution_is_lazy(): + from sumo.app.dora import simulation as simulation_module + + # G1Simulation is defined locally in sumo + assert hasattr(simulation_module, "G1Simulation") + # SimulationNode overrides _init_sim for G1 and lazy policy loading + assert hasattr(simulation_module.SimulationNode, "_init_sim") + + +def test_sumo_task_filtering(): + from sumo.tasks import SUMO_TASK_NAMES, get_sumo_registered_tasks + + available_tasks = get_sumo_registered_tasks() + assert set(SUMO_TASK_NAMES) <= set(available_tasks) + assert "cartpole" not in available_tasks + assert "cylinder_push" not in available_tasks + + +@pytest.mark.g1_extensions +def test_g1_extensions_import(): + from g1_extensions import G1Rollout, sim_g1 + + assert G1Rollout is not None + assert sim_g1 is not None diff --git a/tests/test_spot_asset_resolution.py b/tests/test_spot_asset_resolution.py new file mode 100644 index 0000000..f993450 --- /dev/null +++ b/tests/test_spot_asset_resolution.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest +from judo.tasks.spot.spot_base import SpotBase as JudoSpotBase +from judo.tasks.spot.spot_box_push import SpotBoxPush as JudoSpotBoxPush + +from sumo import MODEL_PATH as SUMO_MODEL_PATH +from sumo.tasks.spot.spot_base import SpotBase as SumoSpotBase +from sumo.tasks.spot.spot_box_push import SpotBoxPush as SumoSpotBoxPush +from sumo.tasks.spot.spot_chair_push import SpotChairPush + + +def _mesh_file(task: object, mesh_name: str) -> str: + for mesh in task.spec.meshes: + if mesh.name == mesh_name: + return mesh.file + raise AssertionError(f"Mesh {mesh_name!r} not found") + + +def _texture_file(task: object, texture_name: str) -> str: + for texture in task.spec.textures: + if texture.name == texture_name: + return texture.file + raise AssertionError(f"Texture {texture_name!r} not found") + + +def test_sumo_spot_base_matches_judo_robot_assets() -> None: + sumo_task = SumoSpotBase() + judo_task = JudoSpotBase() + + assert _mesh_file(sumo_task, "body_0_visual") == _mesh_file(judo_task, "body_0_visual") + assert _mesh_file(sumo_task, "front_left_hip_visual") == _mesh_file(judo_task, "front_left_hip_visual") + assert _texture_file(sumo_task, "bdai_texture") == _texture_file(judo_task, "bdai_texture") + + +def test_wrapped_spot_task_matches_judo_robot_assets() -> None: + sumo_task = SumoSpotBoxPush() + judo_task = JudoSpotBoxPush() + + assert _mesh_file(sumo_task, "body_0_visual") == _mesh_file(judo_task, "body_0_visual") + assert _mesh_file(sumo_task, "arm_link_sh0_visual") == _mesh_file(judo_task, "arm_link_sh0_visual") + assert _texture_file(sumo_task, "bdai_texture") == _texture_file(judo_task, "bdai_texture") + + +def test_custom_sumo_spot_task_keeps_local_object_meshes() -> None: + task = SpotChairPush() + + expected = SUMO_MODEL_PATH / "meshes" / "yellow_chair" / "meshes" / "visual" / "visual_v2.obj" + assert Path(_mesh_file(task, "yellow_chair_mesh")) == expected + + +def test_sumo_spot_tasks_fail_clearly_without_robot_descriptions(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setitem(sys.modules, "robot_descriptions", None) + + with pytest.raises(RuntimeError, match="robot_descriptions"): + SumoSpotBase() diff --git a/tests/test_tasks.py b/tests/test_tasks.py new file mode 100644 index 0000000..cfd170b --- /dev/null +++ b/tests/test_tasks.py @@ -0,0 +1,250 @@ +import numpy as np +import pytest +from judo.tasks import get_registered_tasks + +from sumo.tasks.g1.g1_base import G1Base, G1BaseConfig +from sumo.tasks.g1.g1_box import G1Box, G1BoxConfig +from sumo.tasks.g1.g1_chair_push import G1ChairPush, G1ChairPushConfig +from sumo.tasks.g1.g1_door import G1Door, G1DoorConfig +from sumo.tasks.g1.g1_table_push import G1TablePush, G1TablePushConfig +from sumo.tasks.spot.spot_barrier_drag import SpotBarrierDrag, SpotBarrierDragConfig +from sumo.tasks.spot.spot_barrier_upright import SpotBarrierUpright, SpotBarrierUprightConfig +from sumo.tasks.spot.spot_base import SpotBase, SpotBaseConfig +from sumo.tasks.spot.spot_box_push import SpotBoxPush, SpotBoxPushConfig +from sumo.tasks.spot.spot_chair_push import SpotChairPush, SpotChairPushConfig +from sumo.tasks.spot.spot_chair_ramp import SpotChairRamp, SpotChairRampConfig +from sumo.tasks.spot.spot_cone_push import SpotConePush, SpotConePushConfig +from sumo.tasks.spot.spot_cone_upright import SpotConeUpright, SpotConeUprightConfig +from sumo.tasks.spot.spot_rack_push import SpotRackPush, SpotRackPushConfig +from sumo.tasks.spot.spot_rugged_box_push import SpotRuggedBoxPush, SpotRuggedBoxPushConfig +from sumo.tasks.spot.spot_tire_push import SpotTirePush, SpotTirePushConfig +from sumo.tasks.spot.spot_tire_rack_drag import SpotTireRackDrag, SpotTireRackDragConfig +from sumo.tasks.spot.spot_tire_roll import SpotTireRoll, SpotTireRollConfig +from sumo.tasks.spot.spot_tire_stack import SpotTireStack, SpotTireStackConfig +from sumo.tasks.spot.spot_tire_upright import SpotTireUpright, SpotTireUprightConfig + +G1_TASK_CONFIGS = [ + (G1Base, G1BaseConfig, 3), + (G1Box, G1BoxConfig, 3), + (G1ChairPush, G1ChairPushConfig, 3), + (G1Door, G1DoorConfig, 7), + (G1TablePush, G1TablePushConfig, 11), +] + +SPOT_TASK_CONFIGS = [ + (SpotBase, SpotBaseConfig), + (SpotBoxPush, SpotBoxPushConfig), + (SpotChairPush, SpotChairPushConfig), + (SpotConePush, SpotConePushConfig), + (SpotRackPush, SpotRackPushConfig), + (SpotTirePush, SpotTirePushConfig), + (SpotConeUpright, SpotConeUprightConfig), + (SpotChairRamp, SpotChairRampConfig), + (SpotBarrierUpright, SpotBarrierUprightConfig), + (SpotBarrierDrag, SpotBarrierDragConfig), + (SpotTireRoll, SpotTireRollConfig), + (SpotTireUpright, SpotTireUprightConfig), + (SpotTireStack, SpotTireStackConfig), + (SpotTireRackDrag, SpotTireRackDragConfig), + (SpotRuggedBoxPush, SpotRuggedBoxPushConfig), +] + +REGISTERED_SPOT_TASK_NAMES = [ + "spot_base", + "spot_box_push", + "spot_chair_push", + "spot_cone_push", + "spot_rack_push", + "spot_tire_push", + "spot_cone_upright", + "spot_chair_ramp", + "spot_barrier_upright", + "spot_barrier_drag", + "spot_tire_roll", + "spot_tire_upright", + "spot_tire_stack", + "spot_tire_rack_drag", + "spot_rugged_box_push", +] + + +def _make_g1_rollout_inputs(task, seed: int = 0): + rng = np.random.default_rng(seed) + batch_size = 4 + horizon = 10 + states = rng.standard_normal((batch_size, horizon + 1, task.model.nq + task.model.nv)) + sensors = rng.standard_normal((batch_size, horizon, task.model.nsensordata)) + controls = rng.standard_normal((batch_size, horizon, task.nu)) + return states, sensors, controls + + +def _make_spot_rollout_inputs(task, seed: int = 0): + rng = np.random.default_rng(seed) + batch_size = 4 + horizon = 10 + states = rng.standard_normal((batch_size, horizon, task.model.nq + task.model.nv)) + sensors = rng.standard_normal((batch_size, horizon, task.model.nsensordata)) + controls = rng.standard_normal((batch_size, horizon, task.nu)) + return states, sensors, controls + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_instantiation(task_cls, config_cls, expected_nu): + task = task_cls() + assert task.model is not None + assert task.nu == expected_nu + assert task.config_t is config_cls + assert isinstance(task.config, config_cls) + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_reset(task_cls, config_cls, expected_nu): + task = task_cls() + task.reset() + np.testing.assert_array_equal(task.data.qpos, task.reset_pose) + np.testing.assert_array_equal(task.data.qvel, np.zeros(task.model.nv)) + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_to_sim_ctrl(task_cls, config_cls, expected_nu): + task = task_cls() + controls = np.zeros(expected_nu) + result = task.task_to_sim_ctrl(controls) + assert result.shape == (17,) + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_reward_shape(task_cls, config_cls, expected_nu): + task = task_cls() + states, sensors, controls = _make_g1_rollout_inputs(task) + reward = task.reward(states, sensors, controls) + assert reward.shape == (states.shape[0],) + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_pre_rollout(task_cls, config_cls, expected_nu): + task = task_cls() + task.reset() + curr_state = np.concatenate([task.data.qpos, task.data.qvel]) + task.pre_rollout(curr_state) + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_ctrlrange(task_cls, config_cls, expected_nu): + task = task_cls() + ctrlrange = task.ctrlrange + assert ctrlrange.shape == (expected_nu, 2) + assert np.all(ctrlrange[:, 0] <= ctrlrange[:, 1]) + + +@pytest.mark.parametrize( + "task_cls,config_cls,expected_nu", + G1_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_g1_task_actuator_ctrlrange(task_cls, config_cls, expected_nu): + task = task_cls() + ctrlrange = task.actuator_ctrlrange + assert ctrlrange.shape == (expected_nu, 2) + assert np.all(ctrlrange[:, 0] <= ctrlrange[:, 1]) + + +@pytest.mark.parametrize( + "task_cls,config_cls", + SPOT_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_spot_task_instantiation(task_cls, config_cls): + task = task_cls() + assert task.model is not None + assert task.nu > 0 + + +@pytest.mark.parametrize( + "task_cls,config_cls", + SPOT_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_spot_task_reset(task_cls, config_cls): + task = task_cls() + task.reset() + assert len(task.data.qpos) == task.model.nq + np.testing.assert_array_equal(task.data.qvel, np.zeros(task.model.nv)) + + +@pytest.mark.parametrize( + "task_cls,config_cls", + SPOT_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_spot_task_to_sim_ctrl(task_cls, config_cls): + task = task_cls() + result = task.task_to_sim_ctrl(np.zeros(task.nu)) + assert result.shape[-1] == 25 + + +@pytest.mark.parametrize( + "task_cls,config_cls", + SPOT_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_spot_task_reward_shape(task_cls, config_cls): + task = task_cls() + states, sensors, controls = _make_spot_rollout_inputs(task) + reward = task.reward(states, sensors, controls) + assert reward.shape == (states.shape[0],) + + +@pytest.mark.parametrize( + "task_cls,config_cls", + SPOT_TASK_CONFIGS, + ids=lambda x: x.__name__ if hasattr(x, "__name__") else str(x), +) +def test_spot_task_ctrlrange(task_cls, config_cls): + task = task_cls() + ctrlrange = task.actuator_ctrlrange + assert ctrlrange.shape == (task.nu, 2) + assert np.all(ctrlrange[:, 0] <= ctrlrange[:, 1]) + + +@pytest.mark.parametrize("task_name", REGISTERED_SPOT_TASK_NAMES) +def test_registered_spot_task_instantiation(task_name): + task_cls, config_cls = get_registered_tasks()[task_name] + task = task_cls() + assert task.model is not None + assert isinstance(task.config, config_cls) + + +@pytest.mark.g1_extensions +def test_g1_controller_uses_g1_backend(): + from sumo.controller import make_controller + from sumo.utils.mujoco import G1RolloutBackend + + controller = make_controller("g1_box", "cem") + + assert isinstance(controller.rollout_backend, G1RolloutBackend) + assert controller.action_normalizer.dim == controller.task.nu