A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation
This addresses safety concerns for real-world robot manipulation, but it is incremental as it builds on existing RL methods with added safety mechanisms.
The paper tackles the safety problem of reinforcement learning in contact-rich robot manipulation by proposing a contact-safe framework that detects collisions and limits contact forces, achieving small contact forces in both task and joint spaces during real-world wiping tasks even under unseen scenarios.
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 manipulation, which maintains safety in both the task space and joint space. When the RL policy causes unexpected collisions between the robot arm and the environment, our framework is able to immediately detect the collision and ensure the contact force to be small. Furthermore, the end-effector is enforced to perform contact-rich tasks compliantly, while keeping robust to external disturbances. We train the RL policy in simulation and transfer it to the real robot. Real world experiments on robot wiping tasks show that our method is able to keep the contact force small both in task space and joint space even when the policy is under unseen scenario with unexpected collision, while rejecting the disturbances on the main task.