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.

翻译：自主轮腿机器人有望变革物流系统，提升城市环境中的运行效率与适应性。然而，城市环境中的导航对机器人提出了独特挑战，需要创新的运动与导航解决方案。这些挑战包括：需具备跨多变地形的自适应运动能力，以及高效绕行复杂动态障碍物的导航能力。本文提出了一个完全集成的系统，包含自适应运动控制、运动感知局部导航规划，以及城市范围内的大规模路径规划。通过无模型强化学习技术与特权学习，我们开发了一种多功能运动控制器。该控制器实现了在多种崎岖地形上的高效鲁棒运动，并通过行走与驱动模式间的平滑切换加以辅助。通过分层强化学习框架，该控制器与一个学习型导航控制器紧密集成，从而实现了在挑战性地形及各类障碍物中的高速高效导航。我们的控制器被集成至一个大规模城市导航系统中，并在瑞士苏黎世与西班牙塞维利亚进行了千米级自主导航任务验证。这些任务证明了系统的鲁棒性与适应性，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了轮腿机器人与分层强化学习在自主导航中的可行性，对最后一公里配送等领域具有深远意义。