用路点控制四足机器人，让其自主避障跨越复杂地形。
Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface
- 以路点为接口，训练可自适应的四足运动策略。
- 在仿真与真实场景中成功完成复杂地形导航任务。
- 兼容大语言模型等规划工具，适合多场景应用。
四足机器人通过强化学习展现了卓越的运动能力，包括极端跑酷动作。然而，如何将运动技能与导航结合尚未充分研究，这有望提升长距离移动能力。本文提出 Skill-Nav，一种基于路点接口的分层导航方法，将四足运动技能融入高层规划。具体而言，我们使用深度强化学习训练了一个路点引导的运动策略，使机器人能自主调整步态到达目标位置并避开障碍物。相比直接速度指令，路点提供更简洁且灵活的接口，便于高层规划与底层控制协同。该接口支持多种通用规划工具，如大语言模型（LLMs）和路径规划算法，指导机器人在多样化障碍环境中行进。大量仿真与真实世界实验表明，Skill-Nav 能有效穿越复杂地形，完成高难度导航任务。
原文摘要 · Abstract (English)
Quadrupedal robots have demonstrated exceptional locomotion capabilities through Reinforcement Learning (RL), including extreme parkour maneuvers. However, integrating locomotion skills with navigation in quadrupedal robots has not been fully investigated, which holds promise for enhancing long-distance movement capabilities. In this paper, we propose Skill-Nav, a method that incorporates quadrupedal locomotion skills into a hierarchical navigation framework using waypoints as an interface. Specifically, we train a waypoint-guided locomotion policy using deep RL, enabling the robot to autonomously adjust its locomotion skills to reach targeted positions while avoiding obstacles. Compared with direct velocity commands, waypoints offer a simpler yet more flexible interface for high-level planning and low-level control. Utilizing waypoints as the interface allows for the application of various general planning tools, such as large language models (LLMs) and path planning algorithms, to guide our locomotion policy in traversing terrains with diverse obstacles. Extensive experiments conducted in both simulated and real-world scenarios demonstrate that Skill-Nav can effectively traverse complex terrains and complete challenging navigation tasks.
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