A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation
Xiang Zhu,Shucheng Kang,Jianyu Chen,Xiang Zhu,Shucheng Kang,Jianyu Chen
Reinforcement learning shows great potential to solve complex contact-rich robot manipulation tasks. However, the safety of using RL in the real world is a crucial problem, since unexpected dangerous collisions might happen when the RL policy is imperfect during training or in unseen scenarios. In this paper, we propose a contact-safe reinforcement learning framework for contact-rich robot manipul...