Refine plan demo

examples/overarching_tutorial/simulation/.docker/Dockerfile

@@ -11,7 +11,8 @@ RUN apt-get update && \
     libxi-dev libxkbcommon-dev libxkbcommon-x11-dev libxrender-dev \
     libnss3 libasound2t64 libxkbfile1 \
     python3-pip python3-tk python3-wrapt python3-inflection \
-    ros-jazzy-example-interfaces ros-jazzy-rqt-service-caller
+    ros-jazzy-example-interfaces ros-jazzy-rqt-service-caller \
+    tmux
 
 # Create a ROS 2 workspace.
 RUN mkdir -p /convince_ws/src/
@@ -25,7 +26,14 @@ RUN pip3 install --break-system-packages -r requirements.txt
 # RUN source /opt/ros/jazzy/setup.bash && \
 #     rosdep install --from-paths src -y --ignore-src
 # (pyrobosim)
-RUN git clone -b 4.3.3 https://github.com/sea-bass/pyrobosim.git src/pyrobosim
+
+RUN git clone -b 4.3.4 https://github.com/sea-bass/pyrobosim.git src/pyrobosim
+
+# Install REFINE-PLAN
+RUN git clone -b overarching_demo https://github.com/convince-project/refine-plan.git src/refine-plan
+RUN cd src/refine-plan; pip3 install --break-system-packages -r requirements.txt
+RUN cd src/refine-plan; pip3 install --break-system-packages .
+RUN rm -r src/refine-plan
 
 # # (smc_storm)
 # RUN curl -O -L https://github.com/convince-project/smc_storm/releases/download/0.0.6/smc_storm_executable.tar.gz && \

examples/overarching_tutorial/simulation/refine_plan_demo/LICENSE

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examples/overarching_tutorial/simulation/refine_plan_demo/README.md

@@ -0,0 +1,124 @@
+# REFINE-PLAN Demo
+
+This demo shows the robot executing refined behaviours within the Pyrobosim simulation.
+
+## Building and Running the Docker Container for this Tutorial
+
+> [!WARNING]
+> You must be in the `data-model/examples/overarching_tutorial/simulation` folder for this to work.
+
+### Building the Docker container
+
+```bash
+docker build -t convince_tutorial -f .docker/Dockerfile .
+```
+
+### Running the Docker Container
+
+```bash
+docker run -it --rm\
+    --name convince_tutorial\
+    -v /tmp/.X11-unix:/tmp/.X11-unix:rw\
+    -v ${XAUTHORITY:-$HOME/.Xauthority}:/root/.Xauthority\
+    -v ./tutorial_sim:/convince_ws/src/tutorial_sim\
+    -v ./refine_plan_demo:/convince_ws/src/refine_plan_demo\
+    -v ./.docker/build:/convince_ws/build\
+    -e DISPLAY\
+    -e QT_X11_NO_MITSHM=1\
+    convince_tutorial\
+    bash 
+```
+
+## The Problem
+
+The robot must search for a loaf of bread as quickly as possible within the following house environment:
+
+![pyrobosim](images/pyrobosim.png)
+
+The bread appears stochastically at:
+* The **dining table** with probability **0.5**
+* The **fridge** with probability **0.1**
+* The **kitchen table** with probability **0.1**
+* The **side table** with probability **0.3**
+
+From this, it is clear that the bread is much more likely to appear in the dining room than the kitchen.
+
+## The Initial BT
+We begin with an initial BT for this problem which does not consider uncertainty.
+In this instance, the BT does not know the bread's location distribution and so searches for it by moving 
+**clockwise** around the environment until it finds it.
+This is inefficient, as the robot searches the lower probability locations first.
+A portion of the initial BT for the fridge can be seen below. The sequence node is repeated for the kitchen table, side table, and dining table.
+
+![initial_bt](images/refine_plan_tutorial_initial_bt.png)
+
+### Running the Initial BT
+After starting the Docker container, run:
+
+
+```bash
+cd src/refine_plan_demo/bringup
+./run_demo.sh initial
+```
+
+From this, a tmux session will begin with two windows.
+The first window is for the simulation, and the second is for execution.
+The execution window provides insights into the robot's decision-making.
+Upon running the demo the robot will begin searching for the bread by moving clockwise through the house.
+
+## REFINE-PLAN
+REFINE-PLAN is an automated tool for refining hand-designed BTs to improve robot performance under uncertainty.
+We give an overview of the REFINE-PLAN framework in the image below:
+
+![framework](images/framework.png)
+
+REFINE-PLAN begins with an initial hand-designed BT and a simulator as input. 
+From the initial BT we extract a state space for planning using the condition nodes and blackboard.
+Next, we learn two Bayesian networks per action node in simulation which capture the stochastic transition and cost dynamics of robot execution.
+We then construct a Markov decision process using the learned Bayesian networks and extracted state space, and solve it using standard techniques to synthesise a policy.
+This policy can then be converted back to a BT.
+
+### State Space Extraction
+For this problem, we extract the following state factors:
+* The robot's location in the house (hall, fridge, kitchen table, side table, dining table)
+* A state factor for each location declaring whether the bread is present, or whether this information is unknown.
+
+### Data Collection for Model Learning
+To learn the stochastic transition and cost dynamics of the environment we require a **data collection** phase in simulation.
+Here the robot executes actions randomly in the simulator. The recorded transitions are then used to learn the Bayesian networks.
+
+To run data collection, you can run the following after starting the Docker container, where `<MONGO_CONNECTION_STRING>` is a connection string to a Mongo database, e.g. `localhost:27017`:
+
+```bash
+cd src/refine_plan_demo/bringup
+./run_data_collection.sh <MONGO_CONNECTION_STRING>
+```
+
+### Synthesising the Refined Behaviour
+Given the recorded transitions from the data collection phase, we can then:
+* Learn the Bayesian networks for each BT action node
+* Construct the MDP
+* Solve it using standard techniques
+
+These three steps can be executed for this problem by running the following [script](https://github.com/convince-project/refine-plan/blob/main/bin/overarching_demo_planning.py).
+After running REFINE-PLAN, the robot learns the location distribution for the bread and prioritises searching higher probability locations first.
+Visually, the robot now moves **anticlockwise** through the house, finding the bread quicker on average.
+
+### Running the Refined Behaviour
+After starting the Docker container, run:
+
+```bash
+cd src/refine_plan_demo/bringup
+./run_demo.sh refined
+```
+
+From this, a tmux session will begin with two windows.
+The first window is for the simulation, and the second is for execution.
+The execution window provides insights into the robot's decision-making.
+Upon running the demo the robot will begin searching for the bread by moving **anticlockwise** through the house.
+
+## Further Information
+For more information on REFINE-PLAN, you can:
+* Visit our official [documentation](https://convince-project.github.io/refine-plan/)
+* Look at the [source code](https://github.com/convince-project/refine-plan)
+* Read the [paper](https://ieeexplore.ieee.org/document/11246986) 

examples/overarching_tutorial/simulation/refine_plan_demo/bringup/run_data_collection.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+# Runs the simulation 100 times with proper clean up for data collection
+
+MONGO_CONNECTION_STRING=$1
+
+for RUN in {0..99..1};
+do
+    echo "STARTING DATA COLLECTION RUN $(($RUN + 1))/100"
+    source ./start_sim.sh data $MONGO_CONNECTION_STRING
+    sleep 20s # Wait for everything to be properly setup before waiting
+    sh ./termination_checker.sh
+done

examples/overarching_tutorial/simulation/refine_plan_demo/bringup/run_demo.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+MODE=$1
+
+# Runs the refined or initial policy in simulation
+./start_sim.sh $MODE no_mongo

examples/overarching_tutorial/simulation/refine_plan_demo/bringup/start_sim.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+# Starts up the refine plan demo
+
+SESSION=refine_plan_demo
+POLICY_EXECUTOR_MODE=$1
+MONGO_CONNECTION_STRING=$2
+
+
+tmux -2 new-session -d -s $SESSION
+tmux rename-window -t $SESSION 'pyrobosim'
+tmux new-window -t $SESSION -n 'policy-executor'
+
+tmux select-window -t $SESSION:pyrobosim
+tmux send-keys "ros2 run tutorial_sim run --ros-args -p detect_succ_prob:=1.0" C-m
+
+tmux select-window -t $SESSION:policy-executor
+tmux send-keys "ros2 launch refine_plan_demo policy_executor.launch.py db_collection:=demo-$POLICY_EXECUTOR_MODE mode:=$POLICY_EXECUTOR_MODE db_connection_string:=$MONGO_CONNECTION_STRING" C-m