WAVE-0: 軽量な潜在未来予測でVLA方策と世界行動モデルを橋渡し
WAVE-0: Bridging VLA Policies and WAMs with Lightweight Latent Future Prediction
事前学習済みVLAに軽量な潜在世界モデルを組み合わせ、将来予測を行動生成に直接活用することで、ロボットマニピュレーション性能を大幅に向上させた研究。
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著者: Haomin Wang, Anqiong Ji, Ke Wang, Zhongshan Liu, Ru Ying, Ran Zheng, Zhipeng Zhang
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) policies excel at generalist robotic manipulation but generate actions without explicitly modeling their environmental consequences. World action models (WAMs) incorporate future prediction, yet most rely on costly video generation, while latent alternatives either use predicted futures only as auxiliary targets or infer latent actions from suboptimal visual transitions through costly large-scale video pretraining. We introduce WAVE-0 (World-model-Augmented VLA Execution), a framework that bridges VLAs and WAMs through a lightweight latent world model. By connecting the semantic understanding of pretrained VLAs with explicit future prediction, WAVE-0 allows anticipated action consequences to directly guide action generation. Built on a pretrained VLA, its 257M world model translates VLM-generated latent action plans into predicted future representations that condition the action expert, unifying latent planning, future prediction, and action generation. The world model is trained directly on ground-truth robot actions with a counterfactual ranking objective, requiring neither pixel reconstruction nor large-scale video pretraining. WAVE-0 achieves success rates of 91.47% on RoboTwin 2.0 and 98.7% on LIBERO, along with 88.0%, 61.4%, and 75.0% on LIBERO-Plus, LIBERO-Pro, and LIBERO-Para, respectively. On real-world tasks, it improves the success rate of $π_{0.5}$ from 38.3% to 73.3%. These results demonstrate strong manipulation performance and generalization across diverse distribution shifts.