Being-H0.7: 自己中心視点ビデオからの潜在世界行動モデル
Being-H0.7: A Latent World-Action Model from Egocentric Videos
将来のフレーム生成をせずに、潜在空間での未来推論をVLAポリシーに組み込む新しいモデルを提案。訓練時のみ未来情報を使う二重分岐設計により、推論時は現在の観察のみで将来を考慮した行動決定を実現し、シミュレーションと実世界タスクで高性能を達成した。
詳しい要約
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著者: Hao Luo, Wanpeng Zhang, Yicheng Feng, Sipeng Zheng, Haiweng Xu, Chaoyi Xu, Ziheng Xi, Yuhui Fu, Zongqing Lu
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to actions, but sparse action supervision often encourages shortcut mappings rather than representations of dynamics, contact, and task progress. Recent world-action models introduce future prediction through video rollouts, yet pixel-space prediction is a costly and indirect substrate for control, as it may model visual details irrelevant to action generation and introduces substantial training or inference overhead. We present Being-H0.7, a latent world-action model that brings future-aware reasoning into VLA-style policies without generating future frames. Being-H0.7 inserts learnable latent queries between perception and action as a compact reasoning interface, and trains them with a future-informed dual-branch design: a deployable prior branch infers latent states from the current context, while a training-only posterior branch replaces the queries with embeddings from future observations. Jointly aligning the two branches at the latent reasoning space leads the prior branch to reason future-aware, action-useful structure from current observations alone. At inference, Being-H0.7 discards the posterior branch and performs no visual rollout. Experiments across six simulation benchmarks and diverse real-world tasks show that Being-H0.7 achieves state-of-the-art or comparable performance, combining the predictive benefits of world models with the efficiency and deployability of direct VLA policies.
