Skill-Nav：通过路点接口实现多功能四足运动增强导航
Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface
AI总结：
Skill-Nav通过路点接口将四足运动技能与分层导航框架结合，利用深度强化学习训练策略，实现复杂地形中的自主导航与避障，实验验证其有效性。
摘要：
四足机器人通过强化学习（RL）已展现出卓越的运动能力，包括极限跑酷动作。然而，将运动技能与四足机器人的导航相结合尚未得到充分研究，而这一结合有望增强长距离移动能力。在本文中，我们提出Skill-Nav，一种将四足运动技能融入分层导航框架的方法，以路点作为接口。具体而言，我们使用深度强化学习训练一个路点引导的运动策略，使机器人能够自主调整其运动技能以到达目标位置，同时避开障碍物。与直接速度命令相比，路点为高层规划和低层控制提供了更简单但更灵活的接口。利用路点作为接口，可以应用各种通用规划工具，如大型语言模型（LLMs）和路径规划算法，来引导我们的运动策略穿越具有多种障碍的地形。在仿真和真实场景中进行的大量实验表明，Skill-Nav能够有效穿越复杂地形并完成具有挑战性的导航任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
