群不変スキル発見：対称性を考慮したスキル表現による汎化可能な行動獲得
Group-Invariant Unsupervised Skill Discovery: Symmetry-aware Skill Representations for Generalizable Behavior
物理環境の対称性をスキル発見に組み込み、群不変な内在報酬で冗長性を抑えつつ汎化可能な行動プリミティブを獲得する手法を提案。
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: Junwoo Chang, Joseph Park, Roberto Horowitz, Jongmin Lee, Jongeun Choi
分類: cs.RO, cs.LG
原文アブストラクト
Unsupervised skill discovery aims to acquire behavior primitives that improve exploration and accelerate downstream task learning. However, existing approaches often ignore the geometric symmetries of physical environments, leading to redundant behaviors and sample inefficiency. To address this, we introduce Group-Invariant Skill Discovery (GISD), a framework that explicitly embeds group structure into the skill discovery objective. Our approach is grounded in a theoretical guarantee: we prove that in group-symmetric environments, the standard Wasserstein dependency measure admits a globally optimal solution comprised of an equivariant policy and a group-invariant scoring function. Motivated by this, we formulate the Group-Invariant Wasserstein dependency measure, which restricts the optimization to this symmetry-aware subspace without loss of optimality. Practically, we parameterize the scoring function using a group Fourier representation and define the intrinsic reward via the alignment of equivariant latent features, ensuring that the discovered skills generalize systematically under group transformations. Experiments on state-based and pixel-based locomotion benchmarks demonstrate that GISD achieves broader state-space coverage and improved efficiency in downstream task learning compared to a strong baseline.
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