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 developed 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.

中文翻译：

---

学习轮腿机器人强大的自主导航和运动

自主轮腿机器人有潜力改变物流系统，提高运营效率和城市环境适应性。然而，在城市环境中导航对机器人提出了独特的挑战，需要创新的运动和导航解决方案。这些挑战包括跨不同地形的自适应运动的需要以及在复杂的动态障碍物周围有效导航的能力。这项工作介绍了一个完全集成的系统，包括自适应运动控制、移动感知本地导航规划和城市内的大规模路径规划。使用无模型强化学习（RL）技术和特权学习，我们开发了一种多功能运动控制器。通过步行和驾驶模式之间的平滑过渡，该控制器可在各种​​崎岖地形上实现高效、稳健的运动。它通过分层强化学习框架与学习导航控制器紧密集成，从而能够高速有效地导航穿过具有挑战性的地形和各种障碍物。我们的控制器集成到大型城市导航系统中，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验证。这些任务展示了系统的稳健性和适应性，强调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机器人和分层强化学习用于自主导航的可行性，对最后一英里交付及其他领域具有影响。