Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion

• URL: http://arxiv.org/abs/2503.08375v1
• Date: Tue, 11 Mar 2025 12:32:06 GMT
• Title: Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion
• Authors: Nico Bohlinger, Jonathan Kinzel, Daniel Palenicek, Lukasz Antczak, Jan Peters,
• Abstract summary: On-robot Reinforcement Learning is a promising approach to train embodiment-aware policies for legged robots.<n>We present a framework for efficiently learning quadruped locomotion in just 8 minutes of raw real-time training.<n>We demonstrate the robustness of our approach in different indoor and outdoor environments.
• Score: 13.314871831095882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On-robot Reinforcement Learning is a promising approach to train embodiment-aware policies for legged robots. However, the computational constraints of real-time learning on robots pose a significant challenge. We present a framework for efficiently learning quadruped locomotion in just 8 minutes of raw real-time training utilizing the sample efficiency and minimal computational overhead of the new off-policy algorithm CrossQ. We investigate two control architectures: Predicting joint target positions for agile, high-speed locomotion and Central Pattern Generators for stable, natural gaits. While prior work focused on learning simple forward gaits, our framework extends on-robot learning to omnidirectional locomotion. We demonstrate the robustness of our approach in different indoor and outdoor environments.

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