Hardware-Validated RL Morphology Optimization for 2R Manipulator
Overview
A reinforcement learning framework optimizes a 2R manipulator's morphology, reducing joint load by 38.4% and 41.3% on elliptical and rectangular paths, validated on hardware and simulation.
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Environment: simulation
“arXiv:2610.15756v1 Announce Type: new Abstract: 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. We present a reinforcement-learning framework with RSL-RL PPO that optimizes the morphology of a 2R SCARA-type”
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- arXiv Robotics — research abstractsKinematic Reward, Measured Effort: Hardware-Validated RL Morphology Optimization of a Planar 2R Manipulator
The paper introduces a reinforcement learning framework using RSL-RL PPO to optimize the morphology of a 2R SCARA-type manipulator. The method is validated on both simulation and hardware, demonstrating a 38.4% reduction in second joint load for elliptical paths and 41.3% for rectangular paths compared to the worst morphology.
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