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

SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning