Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent by an operator. It combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn an effective corrective control action that is used in a focused search for collision-free trajectories, and to reduce the frequency of triggering automatic emergency braking. This novel setup enables the RL policy to learn safely and directly on mobile robots in a real-world indoor environment, minimizing actual crashes even during training. Our real-world experiments show that, when compared with several baselines, our approach enjoys a higher average speed, lower crash rate, less emergency intervention, smaller computation overhead, and smoother overall control.

翻译：碰撞规避对于移动机器人与智能体在真实世界中安全运行至关重要。本文提出SAFER系统，一种高效稳健的碰撞规避系统，通过修正操作员发送的控制指令提升安全性。该系统融合了真实世界强化学习（RL）、基于搜索的在线轨迹规划以及自动紧急干预（如自动紧急制动AEB）。强化学习的目标是学习有效的修正控制动作，用于聚焦搜索无碰撞轨迹，并降低触发自动紧急制动的频次。这种创新机制使得强化学习策略能够在真实室内环境的移动机器人上安全直接地学习，即便在训练过程中也能最大限度降低实际碰撞事故。真实世界实验表明，与多种基线方法相比，本方法在保持更高平均速度的同时，实现了更低碰撞率、更少紧急干预、更小计算开销以及更平滑的整体控制效果。