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

Code

• github.com/jznmsl/pss-social
  OfficialIn paper★ 3

Abstract

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 3m3m 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 https://github.com/jznmsl/PSS-Social.

Reproductions