论文标题
一个接触良好的机器人操纵的联络安全增强学习框架
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation
论文作者
论文摘要
强化学习表现出巨大的潜力，可以解决复杂的接触率丰富的机器人操纵任务。但是，在现实世界中使用RL的安全是一个至关重要的问题，因为在培训期间或看不见的RL政策是不完善的，可能会发生意外的危险碰撞。在本文中，我们提出了一个接触安全的加固学习框架，用于接触丰富的机器人操纵，该框架在任务空间和关节空间中保持安全性。当RL政策导致机器人组与环境之间的意外碰撞时，我们的框架能够立即检测到碰撞并确保接触力量很小。此外，最终效应器被强制执行，同时对外部干扰保持强大的态度。我们训练RL政策以模拟并将其转移到真正的机器人。关于机器人擦拭任务的现实世界实验表明，即使在策略处于看不见的情况下，我们的方法也能够在任务空间和关节空间中保持较小的态度，同时拒绝对主要任务的干扰。
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.
