URSA extends quality-diversity actor-critic with learned skill spaces, safety constraints, and world-model training, enabling real-world unsupervised skill discovery on a quadruped.
Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion
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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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From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity
URSA extends quality-diversity actor-critic with learned skill spaces, safety constraints, and world-model training, enabling real-world unsupervised skill discovery on a quadruped.