#Autonomous Navigation (17)

Wheels + Legs + Hierarchical RL: Behind ETH Zurich's 8.3 km Autonomous Delivery

A complete wheeled-legged system from ETH Zurich's Robotic Systems Lab (Science Robotics): the low-level locomotion policy, trained by privileged learning (PPO teacher, GRU student imitation) with CPG gait primitives and the state estimator removed, switches autonomously between driving, trotting, and knee-crawling, peaking at 5.0 m/s on flat ground; the high-level navigation policy outputs bounded Beta-distributed velocity commands at 10 Hz, consuming the LLC hidden state and a visited-position buffer for exploration. Trained in WFC procedurally generated navigation worlds with dynamic obstacles, it completed an 8.3 km delivery across Zurich's Glattpark with minimal intervention; in point-goal comparison it was the only method with zero collisions, planned in 0.34 ms (~3 orders of magnitude faster), and achieved a mechanical COT of 0.16 (53% lower, 3x faster than ANYmal).