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. 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.

翻译：在密集人群中导航对移动机器人而言仍是一项重大挑战。其核心问题在于“机器人冻结”现象，即机器人难以找到安全运动路径而停滞于人群之中。为此，我们提出HiCrowd——一种将强化学习（RL）与模型预测控制（MPC）相结合的层级化框架。HiCrowd利用周围行人运动作为引导，使机器人能够与相容的人流方向保持对齐。高层RL策略生成跟随点以引导机器人融入合适的人群组，而低层MPC则通过短时域规划安全地跟踪该引导。该方法将长期人群感知决策与安全的短期执行相结合。我们在离线场景（回放记录的人类轨迹）和在线场景（仿真中人类轨迹根据机器人行为动态更新）下，将HiCrowd与反应式及基于学习的基线方法进行比较。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方面均表现优异，同时有效减少了冻结行为。研究结果揭示：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。