Learning from Hallucinating Critical Points for Navigation in Dynamic Environments
The Learning from Hallucinating Critical Points (LfH-CP) framework generates large, diverse obstacle datasets for motion planning by focusing on 'critical points'—specific times and locations where obstacles must appear for an optimal plan. This self-supervised method avoids mode collapse by factorizing the hallucination into identifying these critical points first, then procedurally generating diverse trajectories that pass through them.