用扩散模型让四足机器人离线学习新步态，无需重训练
Offline Adaptation of Quadruped Locomotion using Diffusion Models
- 采用无分类器引导扩散模型，从无标签数据中提取目标导向行为
- 可在不改变主策略前提下，仅用车载CPU实现新步态的快速适应
- 首次在ANYmal机器人上验证了该方法的硬件有效性
我们提出一种基于扩散模型的四足运动方法，同时解决技能学习与插值的局限性，以及训练后离线适应新运动行为的问题。这是首个将无分类器引导扩散应用于四足运动的框架，并通过从原始未标注数据集中提取目标条件行为证明其有效性。这些能力可与多技能策略兼容，仅需少量修改和极低计算开销，即可在机器人本地CPU上运行。我们在ANYmal四足平台完成了硬件实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
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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