学习轮腿式机器人的鲁棒自主导航与运动控制
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots
AI总结：
本文提出一个集成自适应运动控制、局部导航规划与大规模路径规划的轮腿式机器人系统，采用无模型强化学习和分层强化学习框架，在苏黎世和塞维利亚完成公里级自主导航验证。
摘要：
自主轮腿式机器人有潜力变革物流系统，提升城市环境中的运营效率和适应性。然而，在城市环境中导航给机器人带来了独特挑战，需要创新的运动控制与导航解决方案。这些挑战包括在多样地形上进行自适应运动的需求，以及在复杂动态障碍物周围高效导航的能力。本工作引入了一个完全集成的系统，包括自适应运动控制、移动感知的局部导航规划以及城市内的大规模路径规划。我们利用无模型强化学习（RL）技术和特权学习，开发了一个多功能的运动控制器。该控制器通过行走与驾驶模式之间的平滑切换，在各种崎岖地形上实现了高效且鲁棒的运动。它通过分层强化学习框架与一个学习型导航控制器紧密集成，从而能够在具有挑战性的地形和各种障碍物中以高速实现有效导航。我们的控制器被集成到一个大规模城市导航系统中，并通过在瑞士苏黎世和西班牙塞维利亚进行的自主公里级导航任务进行了验证。这些任务展示了系统的鲁棒性和适应性，凸显了集成控制系统在复杂环境中实现无缝导航的重要性。我们的研究结果支持轮腿式机器人和分层强化学习用于自主导航的可行性，对最后一公里配送及其他领域具有重要意义。
英文摘要：
Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.
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。
