论文标题
用于机器人工业插入任务的元强化学习
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
论文作者
论文摘要
机器人插入任务的特征是接触和摩擦力学，这使得由于未经建模的物理效果而导致传统反馈控制方法具有挑战性。强化学习（RL）是在这种情况下学习控制政策的有前途的方法。但是，RL在探索过程中可能不安全，可能需要大量的现实培训数据，这很昂贵。在本文中，我们研究了如何通过解决模拟工业插入任务的家庭，然后在现实世界中迅速调整策略，从而解决模拟中的大部分问题。我们通过培训代理商，使用少于20个现实世界经验的试验来成功执行挑战性的现实插入任务来证明我们的方法。视频和其他材料可在https://pearl-intertion.github.io/上找到
Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects. Reinforcement learning (RL) is a promising approach for learning control policies in such settings. However, RL can be unsafe during exploration and might require a large amount of real-world training data, which is expensive to collect. In this paper, we study how to use meta-reinforcement learning to solve the bulk of the problem in simulation by solving a family of simulated industrial insertion tasks and then adapt policies quickly in the real world. We demonstrate our approach by training an agent to successfully perform challenging real-world insertion tasks using less than 20 trials of real-world experience. Videos and other material are available at https://pearl-insertion.github.io/
