Overview

Our Reinforcement Learning framework enables learning on-robot locomotion in just 8 minutes of raw training time. By leveraging the efficient CrossQ algorithm along with two control architectures—Joint Target Prediction (JTP) for agile maneuvers and Central Pattern Generators (CPG) for stable, natural gaits—our approach demonstrates robustness in various indoor and outdoor environments.

Indoor Forward Locomotion

Training the CPG and JTP on the forward locomotion task in an indoor office environment. This controlled setting allows our framework to rapidly adapt and refine the robot's gait with minimal external disturbances. The JTP architecture reached forward velocities of up to 0.85 m/s.

Outdoor Forward Locomotion

Training the CPG and JTP on the forward locomotion task in an outdoor pavement environment. Despite the challenges of natural terrain, our approach consistently achieves stable and efficient forward motion with both control architectures.

Outdoor Omnidirectional Locomotion

Training the CPG and JTP on the omnidirectional locomotion task in an outdoor pavement environment. This setup showcases the framework's ability to handle goal-conditioned multidirectional movement and adapt to rough real-world conditions.

Acknowledgments

This project was funded by National Science Centre, Poland, under the OPUS call in the Weave program UMO-2021/43/I/ST6/02711, by the German Science Foundation (DFG) under grant number PE 2315/17-1 and by the project ``Third Wave of AI'', funded by the Excellence Program of the Hessian Ministry of Higher Education, Science, Research and Art.