ContactRL: 接触を伴う人ロボット協調のための安全強化学習に基づく動作計画
ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration
力フィードバックを報酬に組み込み、接触力を抑えつつタスクを遂行する強化学習フレームワークを提案し、制御バリア関数による安全保証と実機での物体受け渡し実験で有効性を示した。
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著者: Sundas Rafat Mulkana, Ronyu Yu, Tanaya Guha, Emma Li
分類: cs.RO
原文アブストラクト
In collaborative human-robot tasks, safety requires not only avoiding collisions but also ensuring safe, intentional physical contact. We present ContactRL, a reinforcement learning (RL) based framework that directly incorporates contact safety into the reward function through force feedback. This enables a robot to learn adaptive motion profiles that minimize human-robot contact forces while maintaining task efficiency. In simulation, ContactRL achieves a low safety violation rate of 0.2\% with a high task success rate of 87.7\%, outperforming state-of-the-art constrained RL baselines. In order to guarantee deployment safety, we augment the learned policy with a kinetic energy based Control Barrier Function (eCBF) shield. Real-world experiments on an UR3e robotic platform performing small object handovers from a human hand across 360 trials confirm safe contact, with measured normal forces consistently below 10N. These results demonstrate that ContactRL enables safe and efficient physical collaboration, thereby advancing the deployment of collaborative robots in contact-rich tasks.
