论文/arXiv 2610.13164·19 天前

Grounded-Dreamer：从具有物理扎根的世界模型合成数据中进行机器人学习

针对机器人操作数据昂贵难扩展，Grounded-Dreamer结合视频世界模型与仿真物理生成带动作合成数据，并在真实世界任务验证。

AI 整理

• 仿真与训练
• 数据与采集
• 具身模型

原文链接：Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding（arXiv 2610.13164 · 发布日期：2026-10-09）

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

针对机器人操作数据昂贵难扩展，Grounded-Dreamer结合视频世界模型与仿真物理生成带动作合成数据，并在真实世界任务验证。

核心要点

• 动机是大规模操作策略需要多样带动作数据，而真实采集昂贵且难扩展。
• 方法从少量真实初始图像重建物体与相机几何，在仿真中对齐并采集有效轨迹。
• 轨迹用于后训练视频世界模型和逆动力学模型，生成逼真操作视频及对应动作。
• 真实机器人策略仅用筛选后的扎根合成轨迹训练，无需人工遥操作示范。

验证范围与局限：验证范围为摘要所述真实世界任务，未说明具体任务数量或机器人配置。

英文摘要 (Abstract)

Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale. Existing methods either depend on human teleoperation, rely on simulation with limited visual realism, or use human videos that lack embodiment-specific annotations. In this paper, we introduce Grounded-Dreamer, a framework for synthesizing physically grounded robot manipulation data with video world models while avoiding human teleoperation demonstrations. Grounded-Dreamer combines the visual realism of generative video models with simulator physics engines. Starting from a small set of real images depicting robot task initial states, our method automatically reconstructs task-relevant objects and camera geometry, aligns them in simulation, and collects physically valid trajectories. These trajectories are used to post-train both a video world model and an inverse dynamics model, enabling the generation of visually realistic robot manipulation videos and corresponding robot actions from real images. To filter physically implausible rollouts, Grounded-Dreamer replays generated trajectories in the reconstructed simulation and retains only those that successfully complete the tasks. The real-world robot policies are trained solely on the grounded synthetic trajectories without any human demonstrations. Empirical results in real-world tasks demonstrate that Grounded-Dreamer can synthesize physically grounded data and substantially facilitate policy learning without requiring human teleoperation effort.

作者：Feng Jiang, Shu Chen, Yuanfei Wang, Yuan Li, Zhenhao Shen, Jiaqi Liang, Yang Chen, Lei Wei, Chen Xie, Ruihai Wu

来源

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1. arXiv 2610.13164：Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding访问 2026-10-21