用点云监督增强足式机器人在狭窄空间的自感知运动能力
PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces
- 通过点云特征提取监督状态估计网络，提升环境感知
- 训练迭代更快，狭窄空间运动更敏捷
- 无需外部传感器，适合复杂受限环境应用
在受限空间（如爬行通道）中，足式机器人运动极具挑战。现有自感知方法因仅依赖地面特征，难以实现有效通行。本文提出一种基于点云监督的自感知强化学习框架。设计状态估计网络，同时推断碰撞状态、地面及空间特征；提出极坐标系下的点云表示与MLP结合的特征提取方法，高效监督网络训练。实验表明，相比现有方法，本方法训练迭代更快，窄空间运动更灵活。该研究提升了足式机器人在无外感知条件下的受限空间通行能力。
原文摘要 · Abstract (English)
Legged locomotion in constrained spaces (called crawl spaces) is challenging. In crawl spaces, current proprioceptive locomotion learning methods are difficult to achieve traverse because only ground features are inferred. In this study, a point cloud supervised RL framework for proprioceptive locomotion in crawl spaces is proposed. A state estimation network is designed to estimate the robot's collision states as well as ground and spatial features for locomotion. A point cloud feature extraction method is proposed to supervise the state estimation network. The method uses representation of the point cloud in polar coordinate frame and MLPs for efficient feature extraction. Experiments demonstrate that, compared with existing methods, our method exhibits faster iteration time in the training and more agile locomotion in crawl spaces. This study enhances the ability of legged robots to traverse constrained spaces without requiring exteroceptive sensors.
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