用强化学习让机器人安全与人协作，降低接触力并保持效率。
ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration
- 通过力反馈将接触安全融入奖励函数，学习自适应运动轨迹。
- 仿真中违规率仅0.2%，任务成功率87.7%，优于现有方法。
- 结合能量屏障函数保障部署安全，实测接触力始终低于10N。
在人机协作任务中，安全不仅要求避免碰撞，还需确保物理接触的安全与意图明确。本文提出ContactRL，一种基于强化学习的框架，通过力反馈直接将接触安全性纳入奖励函数，使机器人能够学习最小化人机接触力的同时保持任务效率的自适应运动规划。仿真结果显示，ContactRL的违规率仅为0.2%，任务成功率达87.7%，显著优于当前最先进的约束强化学习基线。为保障实际部署安全，我们进一步采用基于动能的控制屏障函数（eCBF）对学习到的策略进行防护。在UR3e机器人平台上开展的小物件交接实验共完成360次真实世界测试，验证了接触的安全性，测得法向力始终低于10N。结果表明，ContactRL可实现高效且安全的物理协作，推动协作机器人在高接触任务中的落地应用。
原文摘要 · Abstract (English)
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.
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