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English(EN) Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control

Enfold 方法内化世界模型计算，实现更快的机器人控制

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为 Enfold 的新方法，旨在通过内化世界生成模型的预测计算来改进机器人领域的具身控制。Enfold 的方法不渲染未来场景，而是使用仅当前编码器根据视觉上下文和语言指令预测未来表征。该方法显著降低了动作延迟，与现有方法相比提高了 10.1 倍，并展示了对现实世界干预的适应性。 AI

影响 这项研究通过减少世界模型的计算开销，可能带来更高效、响应更快的机器人系统。

排序理由 详细介绍机器人领域具身控制新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Enfold 方法内化世界模型计算，实现更快的机器人控制

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详细介绍机器人领域具身控制新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · 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 ·

    Enfold：将世界模型想象力折叠成预测性表征，实现超高效具身控制

    arXiv:2607.26657v2 Announce Type: cross Abstract: 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 computat…

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