让机器人通过识别行人腿部运动实现更自然的导航。
Learning Social Robot Navigation By Sensing Human Legs
- 直接从地面激光雷达扫描中解析腿部运动，端到端生成导航指令。
- 在真实机器人上零样本部署，轨迹平滑且符合社交规范。
- 轻量级模拟器支持单卡一小时内完成训练，适合快速迭代。
在人群间移动的机器人通常使用安装在接近地面的2D LiDAR感知环境，此时传感器主要捕捉到的是移动的腿部而非完整人体。然而，多数基于学习的导航方法仍将行人视为简单的圆形等几何形状。本文提出CALF（Convolutional Attention for Leg Features），一种结合卷积层、注意力机制和MLP的端到端神经架构，直接从LiDAR扫描中解读腿部运动并生成安全导航命令。该策略在自研轻量级2D模拟器LegNav中，通过深度强化学习训练，该模拟器融合2D LiDAR射线追踪与新型行人步态模型。结果表明，所提方法在导航性能与社交合规性上优于经典及学习基线方法。真实世界实验中，零样本部署于TurtleBot 4机器人，生成平滑且社会兼容的轨迹。代码采用JAX编写，仅需单张消费级显卡，一小时内即可完成可部署的CALF策略训练。
原文摘要 · Abstract (English)
Robots navigating among pedestrians typically sense their surroundings with a 2D LiDAR mounted close to the ground. At that height, the sensor mostly sees moving legs rather than whole people, yet most learning-based navigation methods still treat pedestrians as simple shapes like circles. This paper addresses that gap with CALF (Convolutional Attention for Leg Features), an end-to-end neural architecture that combines convolutional layers, attention, and MLP to interpret leg motion directly from LiDAR scans and produce safe navigation commands. The CALF policy is trained using deep reinforcement learning algorithms within LegNav, a custom lightweight 2D simulator that combines 2D LiDAR ray tracing with a novel pedestrian gait model. The resulting policy is compared against classical and learning-based baselines in terms of navigation performance and social compliance. The approach is validated through real-world experiments via zero-shot deployment on a TurtleBot 4, yielding smooth and socially compliant trajectories. Written in JAX, the LegNav simulator enables the training of a deployment-ready CALF policy in under an hour on a single consumer GPU.
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