通过感知人腿实现社交机器人导航

中文摘要 AI 辅助

在行人间导航的机器人通常使用安装在近地面的2D激光雷达（LiDAR）感知周围环境。在该高度，传感器大多只能看到移动的腿部而非完整的人，但多数基于学习的导航方法仍将行人视为圆形等简单形状。本文针对这一差距，提出了CALF（Convolutional Attention for Leg Features，腿部特征卷积注意力），这是一种端到端的神经网络架构，结合卷积层、注意力机制和多层感知机（MLP），直接从激光雷达扫描中解读腿部运动并生成安全的导航指令。CALF策略在LegNav中使用深度强化学习算法进行训练，LegNav是一款定制的轻量级2D模拟器，将2D激光雷达光线追踪与新型行人步态模型相结合。将得到的策略在导航性能和社交合规性方面与经典及基于学习的基线进行比较。该方法通过在TurtleBot 4上零样本部署的真实世界实验得到验证，生成了平滑且符合社交规范的轨迹。LegNav模拟器用JAX编写，可在单个消费级GPU上不到一小时完成可部署CALF策略的训练。

英文摘要

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.

发表机构

• University of Siena（锡耶纳大学）
• Uninettuno University（意大利 uniNettuno 大学）

机构由 AI 辅助整理，请以论文原文为准。