用深度强化学习让四足轮式机器人自主走完2.8公里复杂路线。
Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning
- 基于仅依赖本体感知的强化学习策略，扩展至16自由度机器人
- 通过负载分布优化缓解髋关节过热，实现持续运行2.8公里
- 适合对复杂地形自主导航感兴趣的机器人研发人员
四足轮式机器人因其兼具轮式在平坦地面的高效移动与腿部在崎岖地形的适应能力而受到长期关注。然而，其在真实环境中实现长距离自主导航的应用仍十分有限。本文报告了为商用四足轮式机器人Go2-W构建基于深度强化学习（DRL）的运动控制策略与自主导航系统，并成功应用于真实环境中的长距离自主导航。针对运动控制，我们将此前用于四足机器人的仅依赖本体感知的策略扩展至16-DoF的四足轮式机器人。研究发现，轮式运动会导致髋关节负载集中并引发热量积聚，限制持续行驶；通过策略优化实现负载分散，有效缓解了该问题。系统在2025年筑波挑战赛中完成约2.8公里路线的自主导航，涵盖人行道、公园及台阶，未因过热停机。
原文摘要 · Abstract (English)
Legged-wheeled robots have long been studied for their potential to combine the efficient flat-ground mobility of wheels with the rough-terrain capability of legs. However, examples of their application to long-range autonomous navigation in real environments remain limited. This paper reports our effort to build a deep reinforcement learning (DRL) based locomotion controller and an autonomous navigation system for the commercially available legged-wheeled robot Go2-W, and to apply them to long-range autonomous navigation in a real environment. For locomotion control, we extended a proprioception-only policy, which we had previously developed for quadruped robots, to the 16-DoF legged-wheeled robot. We also found that wheeled locomotion concentrates the load on the hip joints and causes heat concentration that hinders sustained travel, and obtained a policy that suppresses it by distributing the load. We evaluated the system at the Tsukuba Challenge 2025, demonstrating that it can autonomously traverse an approximately 2.8 km route including sidewalks, a park, and stairs without stopping due to overheating.
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