Passive-wheel robots learn efficient skating to navigate cluttered spaces
SkateNav: Learning Energy-Efficient Navigation through Adaptive Skating on Passive-Wheel Quadrupedal Robots
Robotics
Summary
Navigating environments with passive-wheel quadrupedal robots is tricky because these robots move by pushing and gliding, which requires careful timing and steering. The authors developed SkateNav, a method that helps the robot learn how to steer and decide when to push or glide to reach goals efficiently. Their approach uses reinforcement learning and adapts the robot’s movement cycle based on the task, improving success and saving energy compared to other methods. They tested it both in simulations and with a real robot across various indoor and outdoor areas without additional training.
What this means in practice
- •For robotics engineers: Enable quadrupedal robots with passive wheels to navigate efficiently in complex indoor and outdoor environments using learned adaptive skating behaviors.
- •For mobile robot developers: Implement energy-saving navigation strategies that adjust propulsion patterns dynamically for legged robots with passive wheels moving on varied terrains.
Authors
Jeil Jeong, Taeyun Kim, Sangjin Kim, Jinyoung Choi, Sung-Eui Yoon
Abstract
Passive-wheel quadrupedal robots offer energy-efficient locomotion through leg-driven propulsion and momentum-driven gliding. However, navigation requires coordinating steering with intermittent propulsion and adapting push-glide timing to task demands. Velocity-tracking objectives may suppress the speed variations inherent in skating, discouraging efficient gliding. To address these challenges, we propose SkateNav, a unified skating-navigation framework based on reinforcement learning. A time-budgeted goal-reaching objective allows the policy to jointly learn steering, propulsion, and their temporal allocation. An adaptive skating cycle structures gliding, pushing, and recovery, while frequency modulation at every control step adjusts phase durations to navigation demands. Simulation experiments demonstrate higher average success rates and lower cost of transport than the evaluated planner-controller baselines, with ablations confirming the contributions of cycle structure and frequency adaptation. Real-world experiments demonstrate zero-shot transfer from simulation to a passive-wheel Mini Cheetah across cluttered indoor spaces, slopes, and paved outdoor surfaces. Supplementary videos are available on the project page: https://ktw1404.github.io/SkateNav/