English(EN) World-Ego Modeling for Embodied Video Generation in Long-Horizon Navigation-Manipulation Tasks

新型全局-自我模型增强具身视频生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员推出了一种新颖的全局-自我模型（World-Ego Model, WEM），用于生成具身智能体执行长时程导航和操作任务的视频。WEM将环境演变（全局）的预测与其动作（自我）的预测分离开来，解决了保持场景一致性和准确遵循指令的挑战。该模型结合了视觉-语言状态预测器、角色条件注意力以及语义路由的专家混合扩散生成器。为了便于评估，研究团队还开发了HTEWorld，这是一个包含超过125,000个视频片段和300个评估轨迹的新数据集和基准。 AI

影响引入了一种新的具身AI视频生成方法，有望提高机器人导航和操作能力。

排序理由这是一篇详细介绍新模型和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zuyao Lin, Jianhui Zhang, Peidong Jia, Xiaoguang Zhao, Shanghang Zhang, Jingdong Wang, Xingyu Chen · 2026-10-09 04:00

面向长时域导航-操作任务具身视频生成的世界-自我建模

arXiv:2605.19957v2 Announce Type: replace-cross Abstract: Embodied video world models typically capture both scene evolution and the robot's behavior, which we refer to as the \emph{world} and the \emph{ego}, respectively. The world and the ego exhibit different underlying dynami…

本文如何被排名

报道来源 [1]

面向长时域导航-操作任务具身视频生成的世界-自我建模

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