论文标题
在人类环境中，可证明安全的强化钢筋学习
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
论文作者
论文摘要
深度加固学习（RL）在操纵器的运动计划中显示出令人鼓舞的结果。但是，没有方法可以保证在基于RL的操纵器控制中，高度动态障碍（例如人类）的安全性。缺乏正式的安全保证阻止了RL在现实世界中的人类环境中的应用。因此，我们提出了一种屏蔽机制，以确保在训练和部署操纵器的RL算法的同时确保ISO验证的人体安全。我们利用对人类和操纵器的快速可及性分析来确保操纵器在人类在其范围内之前完全停止。我们提出的方法可以保证安全性，并通过防止发作终止碰撞来大大提高RL性能。我们使用人类运动捕获数据来证明我们在模拟中提出的方法的性能。
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipulators in real-world human environments. Therefore, we propose a shielding mechanism that ensures ISO-verified human safety while training and deploying RL algorithms on manipulators. We utilize a fast reachability analysis of humans and manipulators to guarantee that the manipulator comes to a complete stop before a human is within its range. Our proposed method guarantees safety and significantly improves the RL performance by preventing episode-ending collisions. We demonstrate the performance of our proposed method in simulation using human motion capture data.
