SLIP-VLA: 視覚言語行動モデルのための単一ステップ潜在想像による方策学習
SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models
VLAモデルに1回のデノイズ更新で未来の潜在表現を生成する「単一ステップ潜在想像」を導入し、幾何・意味特徴との整合と行動条件付き世界モデルで未来を考慮した行動予測を効率化した手法。
詳しい要約
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著者: Tianfu Li, Haoxuan Xu, Wenbo Chen, Haitian Li, Changchuan Yang, Xinhu Zheng, Jun Ma, Yuan Liu, Lujia Wang, Haoang Li
分類: cs.RO
原文アブストラクト
Vision-Language-Action models are increasingly effective for robotic manipulation, yet most predict actions directly from current observations without explicitly modeling future scene evolution. Recent methods introduce future prediction to improve action generation, but dense future modeling often requires expensive iterative denoising, while one-step alternatives can underperform their multi-step counterparts. To reconcile efficient future modeling with strong action performance, we present SLIP-VLA, a policy learning framework that equips VLA models with a Single-Step Latent Imagination for future-aware action prediction. SLIP-VLA obtains temporally dense future latent representations with a single denoising update, and we improve the perceptual sufficiency of these representations by aligning intermediate latents with future geometric and semantic features. We further improve their control sufficiency through action-conditioned latent world modeling and inverse dynamics modeling, explicitly coupling latent transitions with robot actions. SLIP-VLA achieves state-of-the-art performance across diverse simulation benchmarks and real-world manipulation tasks, while its single-step latent imagination takes only 12 ms.
