DSD: Learning Diverse and Reusable Motor Skills via Diffusion Skill Discovery
Read the original on arXiv Machine Learning →The paper introduces Diffusion Skill Discovery (DSD), a method that employs a diffusion model to approximate the entropy gradient of policy-induced state distributions via score matching. This approach encourages the learning of a diverse repertoire of motor skills for high‑dimensional humanoid control, overcoming limitations of prior mutual‑information based methods that rely on indirect state entropy estimates. The discovered skills are effectively reused in hierarchical control and zero‑shot tasks, yielding more complex and agile behaviors than previous skill discovery techniques.
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