Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots

TL;DR

This paper presents an integrated reinforcement learning-based system enabling wheeled-legged robots to perform robust, adaptive navigation and locomotion in complex urban environments, validated through large-scale real-world experiments.

Contribution

It introduces a hierarchical RL framework combining adaptive locomotion control and local navigation planning for urban wheeled-legged robots, a novel integration for complex terrain navigation.

Findings

Successful kilometer-scale navigation in urban settings

Robust locomotion across varied terrains

Effective obstacle avoidance at high speeds

Abstract

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.