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

Understand

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).