TS-WAM: 視覚言語ロボット制御のための時間的潜在行動と世界行動結合
TS-WAM: Temporal Latent Actions and World-Action Coupling for Vision-Language Robot Control
行動ラベルなしの動画も活用し、未来の視覚状態を予測しながら操作行動を生成する軽量な教師-生徒型ワールドアクションモデルを提案。
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著者: Menghao Yin, Jiaju Yin, Xiyue Dong, Lixin Xu, Zhenhui Zhang, Ke Zhang, Yating Feng, Renjing Xu
分類: cs.RO
原文アブストラクト
Many vision-language-action policies rely on action-labeled robot demonstrations and lack an explicit mechanism for predicting future visual states. These limitations hinder learning from action-free videos and can encourage overfitting to demonstration conditions, weakening generalization and robustness. We present TS-WAM, a lightweight Teacher-Student World-Action Model that addresses these challenges through self-supervised temporal learning and explicit future prediction. Its vlm-jepa framework learns from heterogeneous data, including robot demonstrations and action-free human videos, using an EMA target branch without requiring a separately trained external teacher. By coupling vlm-jepa with delta-lwm, TS-WAM learns to predict latent actions from the current observation and instruction and uses them to predict future visual features that guide action generation at inference. A unified end-to-end training architecture jointly optimizes temporal representation learning, visual dynamics, and robot control without a separate latent-action or world-model pretraining stage. The EMA target branch is removed at inference, keeping the deployed policy lightweight. Experiments on LIBERO, LIBERO-Plus, and SimplerEnv, together with real-world manipulation experiments on Franka Research 3 and RevoTron, demonstrate that TS-WAM achieves strong manipulation performance, generalization, and robustness.