Offline Adaptation of Quadruped Locomotion Using Diffusion Models
Reece O'Mahoney, Alexander L. Mitchell, Wanming Yu, Ingmar Posner, Ioannis Havoutis
Abstract
We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills (modes) and of 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 robot's onboard CPU. We verify the validity of our approach with hardware experiments on the ANYmal quadruped platform.
BibTeX
@inproceedings{icra2025_offlineadaptatio,
title = {Offline Adaptation of Quadruped Locomotion Using Diffusion Models},
author = {Reece O'Mahoney and Alexander L. Mitchell and Wanming Yu and Ingmar Posner and Ioannis Havoutis},
booktitle = {ICRA 2025},
year = {2025}
}