SUSD: Structured Unsupervised Skill Discovery through State Factorization 事件

SUSD: Structured Unsupervised Skill Discovery through State Factorization arXiv:2602.01619v2 Announce Type: replace-cross Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards. One of the most common USD approaches is to maximize the Mutual Information (MI) between skill latent variables and states. However, MI-based methods tend to favor simple, static skills due to their invariance properties, limiting the discover