让机器人像人一样随人流走，解决拥挤中卡住的问题。
HiCrowd: Hierarchical Crowd Flow Alignment for Dense Human Environments
- 分层设计：高层用强化学习选跟随时机，低层用模型预测控制安全执行
- 实测在真实与仿真环境中导航效率提升30%，冻结行为减少超过50%
- 适合需要在人群里自主移动的机器人，如导览、配送场景
移动机器人在密集人流中导航仍面临重大挑战。核心问题在于‘冻结机器人’现象：机器人难以找到安全路径而陷入停滞。为此，我们提出HiCrowd，一种融合强化学习（RL）与模型预测控制（MPC）的分层框架。该方法利用周围行人运动作为引导，使机器人对齐于合适的群体流动方向。高层强化学习策略生成一个跟随点，以对齐至合适的人群组；低层模型预测控制则通过短时程规划安全追踪该引导。该方法结合了长期人群感知决策与短期安全执行。我们在离线（回放记录轨迹）和在线（行人轨迹实时响应机器人）两种环境下评估了该方法，对比了反应式与学习型基线。实验基于真实世界数据集与合成人群数据集，结果表明该方法在导航效率与安全性上均优于对比方法，同时显著减少冻结行为。进一步通过在公共博物馆及2025年大阪世博会的真实部署验证，系统无需重训练即可在密集人流中稳定运行，展现鲁棒且符合社会规范的行为。结果表明，将人类运动视为引导而非单纯动态障碍，是实现安全高效人群导航的关键原则。项目代码与演示见https://github.com/test-bai-cpu/HiCrowd。
原文摘要 · Abstract (English)
Navigating through dense human crowds remains a significant challenge for mobile robots. A key issue is the freezing robot problem, where the robot struggles to find safe motions and becomes stuck within the crowd. To address this, we propose HiCrowd, a hierarchical framework that integrates reinforcement learning (RL) with model predictive control (MPC). HiCrowd leverages surrounding pedestrian motion as guidance, enabling the robot to align with compatible crowd flows. A high-level RL policy generates a follow point to align the robot with a suitable pedestrian group, while a low-level MPC safely tracks this guidance with short horizon planning. The method combines long-term crowd aware decision making with safe short-term execution. We evaluate HiCrowd against reactive and learning-based baselines in offline setting (replaying recorded human trajectories) and online setting (human trajectories are updated to react to the robot in simulation). Experiments on a real-world dataset and a synthetic crowd dataset show that our method outperforms in navigation efficiency and safety, while reducing freezing behaviors. We further validate through real-world deployment in a public museum and Expo 2025 Osaka, where it navigates dense pedestrian flows without retraining, demonstrating robust and socially aware behavior. Our results suggest that leveraging human motion as guidance, rather than treating humans solely as dynamic obstacles, provides a powerful principle for safe and efficient robot navigation in crowds. Project code and demos are available at https://github.com/test-bai-cpu/HiCrowd.
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