Safe RL-MPCExpert
RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments
This preprint introduces AC-PAC-NMPC, a hybrid controller that places RL-trained actor, critic, and sensor-prediction models inside a sampling-based stochastic NMPC framework to provide finite-time probabilistic safety bounds and long-horizon vision-based navigation. On a fixed-wing UAV with an onboard depth camera, it raised real-robot success from 40% (both RL actor and best PAC-NMPC baseline) to 80% with the lowest cost; in simulation it reached 90% success versus 85% for the strongest baseline. This matters for agile robots that need learned long-range behavior without sacrificing formal collision-probability guarantees.