Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning

Robots cannot navigate safely if their perception misses hazards. COPPOL solves this by conformalising segmentation outputs to give distribution-free guarantees on hazard coverage, then using these calibrated hazard maps to guide reinforcement learning. The result is safer navigation under imperfect perception, with higher hazard coverage, fewer unsafe violations, greater clearance, and strong…