外观
论文arXiv 2610.1575617 天前
运动学奖励，实测力耗：经硬件验证的平面 2R 机械臂 RL 形态优化
原文链接：Kinematic Reward, Measured Effort: Hardware-Validated RL Morphology Optimization of a Planar 2R Manipulator（arXiv 2610.15756 · 发布日期：2026-10-11）
Kinematic Reward, Measured Effort: Hardware-Validated RL Morphology Optimization of a Planar 2R Manipulator
该工作用以 Yoshikawa 可操作度为核心奖励的 PPO 优化平面 2R 机械臂形态，并在仿真与硬件上验证关节负载降低。
核心要点
- 经典可操作度常用于机械臂形态优化，但多停留在仿真运动学分析，少有硬件验证。
- 框架采用 RSL-RL PPO，以 Yoshikawa 运动学可操作度为核心奖励搜索 2R SCARA 型臂形态。
- 圆形任务用解析闭式最优解验证可恢复性，并扩展到无解析最优的椭圆和矩形路径。
- 硬件验证中，椭圆/矩形第二关节负载降 38.4±0.6%/41.3±0.3%，第一关节降 15%。
验证范围与局限：验证范围为仿真中的圆形、椭圆和矩形路径，以及 2R 硬件机械臂上的椭圆和矩形路径关节负载。
英文摘要 (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 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. Then the same method is applied to custom elliptical and rectangular paths having no analytical optimum. 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%.
作者：Gunesh Gupta, Arvind Kumar Mishra, Sohom Chakrabarty
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