Reach Avoid

Reach-Avoid (RA) problems in control theory focus on designing systems that reliably reach a target state while avoiding unsafe states, a crucial challenge for autonomous systems like robots and vehicles. Current research emphasizes developing robust and scalable solutions using reinforcement learning (RL), particularly deep deterministic policy gradients (DDPG) and model predictive control (MPC) methods, often incorporating control barrier functions (CBFs) for safety guarantees. These advancements are improving the safety and reliability of autonomous systems in complex, dynamic environments, with applications ranging from autonomous navigation to multi-robot coordination and safety-critical industrial processes. Furthermore, research is actively exploring methods for verifying the safety and performance of learned RA controllers, using techniques like neural network reachability analysis and supermartingale verification.