提出新方法让机器人在密集人群中导航不撞人也不卡住，还能适应训练时没见过的拥挤程度。
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
- 用密度无关的编码和随机密度训练，让模型对人群多少不敏感。
- 在21人密集中99%成功到达目标，碰撞率比现有方法低60多点。
- 适合做真实场景下移动机器人导航，尤其拥挤环境应用。
在密集人群中安全导航需要具备超出训练密度的泛化能力。基于学习的导航方法因密度敏感的观测归一化和社会代价缩放，在分布外人群密度下易失效；而解析求解器虽安全但常在紧密交互中冻结。本文提出一种强化学习方法，通过密度不变的观测编码、密度随机训练及物理启发的邻近奖励塑造实现零样本密度泛化。编码方式为距离排序的前K个行人加受限的人群摘要，保持输入统计稳定。在11~16人（3m×3m区域）训练，评估至21人（1.3倍密度），策略在超过99%回合中成功抵达目标，随机人群下86%无碰撞成功率，显著减少冻结现象，且较学习型基准方法提升超60点无碰撞优势。代码已开源。
原文摘要 · Abstract (English)
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 $N\!\in\![11,16]$ pedestrians in a $3\mathrm{m}\times3\mathrm{m}$ arena and evaluated up to $N\!=\!21$ pedestrians ($1.3\times$ 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 \href{https://github.com/jznmsl/PSS-Social}{https://github.com/jznmsl/PSS-Social}.
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