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| # Uses FRANKA_PANDA_CFG (gravity on, stiffness=80, damping=4) instead of HIGH_PD. | ||
| _FRANKA_JOINT_POS_CFG = FRANKA_PANDA_CFG.copy() | ||
| _FRANKA_JOINT_POS_CFG.spawn.usd_path = ( | ||
| f"{ISAACLAB_STAGING_NUCLEUS_DIR}/Arena/assets/robot_library/franka_panda_hand_on_stand.usd" |
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Can it share the same path as above _FRANKA_CFG.spawn.usd_path
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yes they share the same. updated to use _FRANKA_CFG.spawn.usd_path directly
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| class FrankaEmbodiment(EmbodimentBase): | ||
| """Embodiment for the Franka robot.""" | ||
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| name = "franka" |
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Not now. But can you add a TODO?
Rename it to franka_ik, add a base class where action_cfg is missing, and refactor child classes for both franka_joint_pos and franka_ik.
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thanks, great suggestion! Added the todo comment
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Summary
Update the lift object RL example to have a high success rate model
Detailed description
Existing lift object RL training model success rate is low (~30%), and the arm motion is unnatural.
Add a franka joint control embodiment for RL training to avoid the weird arm motion from IK version
Update the observation to include joint and target poses only
Fix a bug in the base rls policy so the target pose (task_obs in addition to policy) is passed to the actor/critic model
Fix a bug resulting ~0 success rate for parallel eval due to incorrect object/target frame in the success term
Update RL docs with latest models and commands
The RL model now gets 70~80% success rate within 1.5h