DeepSkate: Reinforcement Learning of a Robust Controller for Energy Efficient Quadruped Skating
James Florin Petri, Gerard Lacey
AI summary
Problem
Standard legged robots suffer from poor energy efficiency, while passive wheeled-legged skating systems offer great potential but remain difficult to control due to complex dynamics, lack of active steering or braking, and terrain adaptability challenges.
Approach
The authors train an end-to-end reinforcement learning policy in simulation with custom rewards and domain randomization, then transfer it to a physical quadruped equipped with non-steerable passive wheels to learn agile, terrain-adaptive skating gaits.
Key results
- Achieved stable skating speeds up to 1.5 m/s with 95% velocity tracking accuracy
- Reduced cost of transport by 40.9% compared to skating state-of-the-art and 70.9% versus standard legged locomotion
- Successfully navigated flat, rough, sloped, and stepped terrains without active braking or steerable ankles
- Established a new performance benchmark for efficient quadruped skating locomotion
Why it matters
Proves passive wheeled-legged skating is a highly efficient and practical locomotion mode for quadrupedal robots, paving the way for longer-range urban and mixed-terrain deployments.
Abstract
Wheeled-legged hybrid robots have generated grow- ing interest in the research community due to the need for more efficient and versatile locomotion. Most recent research has focused on active wheels, but passive wheeled systems have great potential in improving energy efficiency. However, skating remains highly complex due to the difficulties of balancing dynamic motion, managing wheel-ground interactions, achieving precise torque control for smooth rolling, and adapting to unpre- dictable terrain while maintaining stability. We present an end- to-end model-free reinforcement learning approach that enables quadrupedal robots to skate efficiently, achieving agile and robust locomotion on both flat and rough terrain. Our skating- specific policy and sim-to-real pipeline are validated on a physical quadruped across diverse terrains with varying roughness, slopes, and features, consistently demonstrating controlled and efficient traversal. The robot achieves velocities up to 1.5 m/s with a cost of transport 40.9% lower than the skating state of the art and 70.9% lower than standard legged locomotion. These results establish skating as a feasible and efficient alternative mode of urban locomotion for quadrupedal robots, setting a foundation for future wheeled-legged research.