Kinematic Reward, Measured Effort: Hardware-Validated RL Morphology Optimization of a Planar 2R Manipulator
We present a reinforcement-learning framework with RSL-RL PPO that optimizes the morphology of a 2R SCARA-type manipulator arm with Yoshikawa's kinematic manipulability index as a core reward, and the analytically derived closed-form optimum for the circular task is used to verify that our method recovers it.
ProofPaper ↗
Key points
- Yoshikawa's Manipulability Index is generally used as a classical measure to define an optimal morphology of a manipulator.
- However, any such optimization is almost always studied as a purely kinematic quantity in simulation and is rarely validated on hardware.
- The RL-optimized morphologies are then transferred to a 2R hardware manipulator to validate its effect in reduction of joint load in the hardware.
- Compared to the worst morphology, the optimum morphology delivered by our method reduces second joint load by 38.4 +/- 0.6% for the ellipse path and 41.3 +/- 0.3% for the rectangle, and first joint load by 15%.
Sources (1)
- [1]Kinematic Reward, Measured Effort: Hardware-Validated RL Morphology Optimization of a Planar 2R ManipulatorarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 04:37 PM
We present a reinforcement-learning framework with RSL-RL PPO that optimizes the morphology of a 2R SCARA-type manipulator arm with Yoshikawa's kinematic manipulability index as a core reward, and the analytically derived closed-form optimum for the circular task is used to verify that our method recovers it.
Yoshikawa's Manipulability Index is generally used as a classical measure to define an optimal morphology of a manipulator.
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