スペクトラルスキルによる表現豊かで構成可能な動作表現の学習
Learning Expressive and Compositional Motion Representation via Spectral Skills
予測表現学習で短い動作区間を潜在スキルとして符号化し、ヒューマノイドの追従・連結・合成を可能にする制御インターフェースを提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Feiyang Wu, Chenxiao Gao, Chen Yang, Ye Zhao, Bo Dai, Anqi Wu
分類: cs.RO, cs.LG
原文アブストラクト
Robotic foundation models offer a promising path toward general-purpose humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on the command interface between the planner and the controller, which must support accurate execution while remaining easy to predict, and ideally allow new behaviors to be composed from prior ones. In this work, we introduce spectral skills, a latent representation of this interface that meets these requirements through predictive representation learning. By design, spectral skills compactly encode short motion segments and are learned by predicting subsequent motion rather than reconstructing the encoder input. On a 29-DoF humanoid, a controller conditioned on spectral skills reduces global tracking error by 62\% relative to the state of the art. The same frozen controller chains independently encoded skills without a separate transition policy. It also composes new behaviors by adding orthogonal directions to any compatible base skill, producing combinations unseen in the training data. We demonstrate tracking, chaining, and composition, as well as control through a language-conditioned planner, on Unitree G1 hardware. Project page: https://spectral-skill.github.io