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  • [2602. 15922] World Action Models are Zero-shot Policies
    We introduce DreamZero, a World Action Model (WAM) built upon a pretrained video diffusion backbone Unlike VLAs, WAMs learn physical dynamics by predicting future world states and actions, using video as a dense representation of how the world evolves
  • DreamZero: World Action Models are Zero-shot Policies
    We introduce DreamZero, a World Action Model (WAM) built upon a pretrained video diffusion backbone Unlike VLAs, WAMs learn physical dynamics by jointly predicting future world states and actions, using video as a dense representation of how the world evolves
  • NVIDIA DreamZero: World Action Models Are Zero-Shot Policies
    DreamZero is a World Action Model that jointly predicts actions and videos, achieving strong zero-shot performance on unseen tasks This release package contains everything needed to load a pretrained DreamZero model and run distributed inference via a WebSocket server
  • 世界动作模型是零样本策略 - 知乎
    用“世界动作模型”（World Action Models，简称 WAMs）而非“视频动作模型”（Video Action Models，简称 VAMs），以体现视频只是世界建模的众多目标之一——未来的 WAMs 可能将动作与触觉感知、力反馈或学习的潜表征等其他预测模态相结合。
  • 【论文阅读】DreamZero:World Action Models are Zero-shot . . .
    本文提出了一种名为DreamZero的机器人基础模型，通过同时预测视频和动作（世界动作模型），让机器人能像人类一样通过“脑补”画面来规划动作，从而在从未见过的任务和环境中实现零样本泛化。 _dreamzero: world action models are zero-shot policies
  • World Action Models are Zero-shot Policies - Semantic Scholar
    We introduce DreamZero, a World Action Model (WAM) built upon a pretrained video diffusion backbone Unlike VLAs, WAMs learn physical dynamics by predicting future world states and actions, using video as a dense representation of how the world evolves
  • [论文评述] World Action Models are Zero-shot Policies
    DreamZero是一项关于World Action Model (WAM) 的研究，旨在解决现有Vision-Language-Action (VLA) 模型在泛化到新环境中的未知物理动作和技能方面的局限性。
  • World Action Models are Zero-shot Policies | OpenReview
    Abstract: State-of-the-art Vision-Language-Action (VLA) models excel at semantic generalization but struggle to generalize to unseen physical motions in novel environments We introduce DREAMZERO, a World Action Model (WAM) built on a pretrained video diffusion backbone
  • 【论文阅读】DreamZero:World Action Models are Zero-shot . . .
    本文提出了一种名为DreamZero的机器人基础模型，通过同时预测视频和动作（世界动作模型），让机器人能像人类一样通过"脑补"画面来规划动作，从而在从未见过的任务和环境中实现零样本泛化。 现有的视觉语言动作模型（VLAs）虽然擅长语义理解，但缺乏对物理世界动态（如几何、动力学）的理解，难以泛化到从未见过的新动作或新环境，且通常需要大量重复的演示数据。 采用预训练的视频扩散模型作为骨干，构建了一个名为World Action Model (WAM) 的架构。 该模型通过联合预测未来的视频帧和机器人动作，利用视频预测作为视觉规划器来指导动作生成。
  • Paper page - World Action Models are Zero-shot Policies
    We introduce DreamZero, a World Action Model (WAM) built upon a pretrained video diffusion backbone Unlike VLAs, WAMs learn physical dynamics by predicting future world states and actions, using video as a dense representation of how the world evolves





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