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.

翻译：本文提出一种基于扩散的四足运动方法，该方法同时解决了多技能学习与插值的局限性，以及训练后离线适应新运动模式的问题。这是首个将无分类器引导扩散应用于四足运动的研究框架，并通过从原始未标注数据集中提取目标导向行为验证了其有效性。我们证明这些能力可与多技能策略兼容，且仅需少量修改和最低计算开销即可实现（完全在机器人板载CPU上运行）。通过在ANYmal四足平台上进行硬件实验，我们验证了该方法的有效性。