Enfold: 世界モデルの想像力を予測表現に折り畳み、超効率的な身体制御を実現
Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control
生成モデルが未来を構築する過程で得られる中間計算を、現在の視覚と言語指示のみから予測される表現に転移させる手法を提案。推論時に生成器を実行せずに行動予測が可能となり、遅延を大幅削減。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Weili Zeng, Yitong Xing, Fulong Liu, Chengqun Yang, Antao Xiang, Feng Tian, Jingnan Gao, Jisong Cai, Xin Wang, Xiaomin Wu, Yao Mu, Xiaokang Yang, Yichao Yan
分類: cs.RO, cs.CV
原文アブストラクト
World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a costly generative branch. We argue that their more reusable asset is the computation that constructs a future. As a generator transforms a corrupted future into a coherent trajectory, its intermediate states organize appearance, spatial layout, and interaction across levels of abstraction. Can this future-generative computation be internalized in a representation inferred from the present alone? We present Enfold, which transfers this computation into a representation predicted from the current visual context and language instruction. During training, multi-level states exposed as the generator processes the observed future supervise a current-only encoder. The learned representation is fed back to condition future generation and is read by task heads without allowing task gradients to reshape the encoder. At deployment, action prediction no longer executes the generator. Across LIBERO, RoboTwin2.0, and real-robot tasks, Enfold supports strong control while reducing action latency by $3.7\times$ relative to Fast--WAM, Enfold-Flash reaches $10.1\times$. Representation analyses show that it suppresses nuisance variation and preferentially captures changes that emerge over longer horizons. When the current scene is altered by human intervention, both the generated continuation and the executed actions adapt, which is inconsistent with fixed trajectory replay. These results recast a world generator as a source of predictive control representations: its future need not be materialized at every step if its internal structure can be enfolded into the present.
