Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding
Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding
This paper presents Grounded-Dream, a framework that synthesizes physically grounded robot manipulation data using video world models and simulator physics, without requiring human teleoperation. Starting from real images, the method automatically reconstructs objects and camera geometry, aligns them in simulation, and generates physically valid trajectories. These are used to train video world models and inverse dynamics models, enabling the generation of realistic robot manipulation videos and actions from real images. Empirical results show that Grounded-Dreamer can synthesize physically grounded data and significantly improve policy learning without human demonstrations.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ometry, 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 simulatio
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rounded-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 ge
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Source：arXiv Robotics — research abstracts · arxiv.org