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Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion
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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.
Forward citations
Cited by 2 Pith papers
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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
CrossQ with weight normalization scales to high update-to-data ratios and matches or outperforms reset-based baselines on 25 continuous control benchmarks.
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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.
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