Infusing Reachability-Based Safety into Planning and Control for Multi-agent Interactions
This addresses safety challenges in autonomous systems like self-driving cars, offering a novel integration approach but is incremental in combining existing reachability methods.
The paper tackles the problem of ensuring safety in multi-agent robot interactions by integrating Hamilton-Jacobi reachability theory into both planning and control, demonstrating in a highway scenario that it provides strong safety assurances while achieving the highest performance compared to other safety controllers.
Within a robot autonomy stack, the planner and controller are typically designed separately, and serve different purposes. As such, there is often a diffusion of responsibilities when it comes to ensuring safety for the robot. We propose that a planner and controller should share the same interpretation of safety but apply this knowledge in a different yet complementary way. To achieve this, we use Hamilton-Jacobi (HJ) reachability theory at the planning level to provide the robot planner with the foresight to avoid entering regions with possible inevitable collision. However, this alone does not guarantee safety. In conjunction with this HJ reachability-infused planner, we propose a minimally-interventional multi-agent safety-preserving controller also derived via HJ-reachability theory. The safety controller maintains safety for the robot without unduly impacting planner performance. We demonstrate the benefits of our proposed approach in a multi-agent highway scenario where a robot car is rewarded to navigate through traffic as fast as possible, and we show that our approach provides strong safety assurances yet achieves the highest performance compared to other safety controllers.