A hierarchical reinforcement learning framework lets a quadruped robot automatically switch between trotting, bounding, galloping, and fall recovery based on distance to the goal, with switch distances tuned by CMA-ES.
Offline Adaptation of Quadruped Locomotion using Diffusion Models
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abstract
We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills and of (modes) offline adapting to new locomotion behaviours after training. This is the first framework to apply classifier-free guided diffusion to quadruped locomotion and demonstrate its efficacy by extracting goal-conditioned behaviour from an originally unlabelled dataset. We show that these capabilities are compatible with a multi-skill policy and can be applied with little modification and minimal compute overhead, i.e., running entirely on the robots onboard CPU. We verify the validity of our approach with hardware experiments on the ANYmal quadruped platform.
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Discovery of skill switching criteria for learning agile quadruped locomotion
A hierarchical reinforcement learning framework lets a quadruped robot automatically switch between trotting, bounding, galloping, and fall recovery based on distance to the goal, with switch distances tuned by CMA-ES.