STAR: 回転拡張ベクトル量子化による多様なロボットスキル抽象化の学習
STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector Quantization
VQ-VAEのコードブック崩壊とスキル間因果関係の欠如を解決するため、回転拡張残差スキル量子化と因果スキルトランスフォーマーを組み合わせ、LIBEROベンチマークと実世界タスクで約12%の性能向上を達成した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Hao Li, Qi Lv, Rui Shao, Xiang Deng, Yinchuan Li, Jianye Hao, Liqiang Nie
分類: cs.RO, cs.LG
原文アブストラクト
Transforming complex actions into discrete skill abstractions has demonstrated strong potential for robotic manipulation. Existing approaches mainly leverage latent variable models, e.g., VQ-VAE, to learn skill abstractions through learned vectors (codebooks), while they suffer from codebook collapse and modeling the causal relationship between learned skills. To address these limitations, we present \textbf{S}kill \textbf{T}raining with \textbf{A}ugmented \textbf{R}otation (\textbf{STAR}), a framework that advances both skill learning and composition to complete complex behaviors. Specifically, to prevent codebook collapse, we devise rotation-augmented residual skill quantization (RaRSQ). It encodes relative angles between encoder outputs into the gradient flow by rotation-based gradient mechanism. Points within the same skill code are forced to be either pushed apart or pulled closer together depending on gradient directions. Further, to capture the causal relationship between skills, we present causal skill transformer (CST) which explicitly models dependencies between skill representations through an autoregressive mechanism for coherent action generation. Extensive experiments demonstrate the superiority of STAR on both LIBERO benchmark and realworld tasks, with around 12\% improvement over the baselines.
