generate_dataset Fails After Successful Teleoperation and Annotation With Custom Robot Model #4591
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Yuchili2022
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I encountered some issues while working on Teleoperation and Imitation Learning with Isaac Lab Mimic, and I would like to discuss possible improvements.

Custom robot model configuration
Since I am using my own robot model, I modified the following environment configurations to match my setup:
• stack_joint_pos_env_cfg
• stack_ik_real_env_cfg
• stack_ik_real_mimic_env
• stack_ik_real_mimic_env_cfg
Additionally, because my gripper has been replaced, I also updated the "observation" settings in the stack task accordingly.
Teleoperation and demonstration recording are working correctly
The following steps can all be completed successfully:
• I can run teleop_se3_agent.py and the robot performs correct Cartesian motions at the TCP, including both translation and rotation.
• record_demos works and I can collect 10 successful demonstrations that satisfy the success conditions.
• annotate_demos also runs without issues.
The issue occurs when running generate_dataset
When I run generate_dataset, it always results in failures.
Based on the documentation, high quality demonstrations should follow these principles:
• Keep demonstrations short
• Use direct motion paths
• Avoid pauses and maintain smooth, continuous movement
Based on this, I suspect the following issues in my demonstrations:
a. Keyboard input makes smooth motions difficult
Using the keyboard often results in discrete, non smooth movements or unintended pauses.
b. I need to open two fixed views (front + right) to perform accurate demonstrations
Currently, I open both a front view and a right view simultaneously to better align with objects.
Although this helps with accuracy, it complicates the teleoperation process and may reduce overall smoothness.
Questions I would like to ask:
Does it significantly improve the smoothness and quality of the teleoperation process?
Would you recommend directly using CloudXR for this type of task?
Thank you!
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