Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds

Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-of-distribution crowd sizes due to density-sensitive observation normalization and social-cost scaling, while analytical solvers often remain safe but freeze in tight interactions. We propose a reinforcement learning approach for dense, variable-density navigation that attains zero-shot density generalization using a density-invariant observation encoding with density-randomized training and physics-informed proxemic reward shaping with density-adaptive scaling. The encoding represents the distance-sorted K nearest pedestrians plus bounded crowd summaries, keeping input statistics stable as crowd size grows. Trained with N!∈![11,16] pedestrians in a 3m×3m arena and evaluated up to N!=!21 pedestrians (1.3× denser), our policy reaches the goal in >99% of episodes and achieves 86% collision-free success in random crowds, with markedly less freezing than analytical methods and a >!60-point collision-free margin over learning-based benchmark methods. Codes are available at hrefhttps://github.com/jznmsl/PSS-Socialhttps://github.com/jznmsl/PSS-Social.