SNAP: Semantic Navigation Among Pedestrians
Overview
This paper introduces SNAP, a reinforcement learning navigation policy that jointly handles semantic terrain and dynamic obstacles using a single semantically annotated bird's-eye-view map. The approach avoids explicit human-state estimation and outperforms existing baselines in crowd avoidance and semantic path planning. It demonstrates zero-shot transfer to a legged robot, enabling socially compliant navigation in highly dynamic real-world environments.
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- arXiv Robotics — research abstractsSignalSNAP: Semantic Navigation Among Pedestrians
This paper introduces SNAP, a reinforcement learning navigation policy that jointly handles semantic terrain and dynamic obstacles using a single semantically annotated bird's-eye-view map. The approach avoids explicit human-state estimation and outperforms existing baselines in crowd avoidance and semantic path planning. It demonstrates zero-shot transfer to a legged robot, enabling socially compliant navigation in highly dynamic real-world environments.
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