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.

翻译：在密集人群环境中导航仍是移动机器人的重大挑战。核心难题在于"机器人冻结问题"——机器人因难以找到安全运动路径而陷入人群停滞。为解决该问题，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。HiCrowd利用周围行人的运动作为引导，使机器人能够与兼容的人群流对齐。高层RL策略生成跟随点，将机器人导向合适的人群群组；低层MPC则通过短时域规划安全追踪该引导。该方法结合了长期人群感知决策与安全短期执行。我们在离线场景（回放记录的行人轨迹）和在线场景（仿真中行人轨迹随机器人反应实时更新）下，将HiCrowd与反应式及学习式基线方法进行对比。在真实世界数据集和合成人群数据集上的实验表明，本方法在导航效率与安全性上均表现优越，同时显著减少冻结行为。我们进一步通过公共博物馆及2025年大阪世博会的实地部署验证——无需重新训练即可在密集人流中导航，展现出鲁棒且具备社会认知的行为。研究结果表明：将人类运动视为引导而非单纯动态障碍物，为机器人在人群中的安全高效导航提供了重要原则。项目代码与演示详见https://github.com/test-bai-cpu/HiCrowd。