  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.
