Vidar: Embodied Video Diffusion Model for Generalist Bimanual Manipulation

• URL: http://arxiv.org/abs/2507.12898v2
• Date: Sun, 27 Jul 2025 13:48:18 GMT
• Title: Vidar: Embodied Video Diffusion Model for Generalist Bimanual Manipulation
• Authors: Yao Feng, Hengkai Tan, Xinyi Mao, Guodong Liu, Shuhe Huang, Chendong Xiang, Hang Su, Jun Zhu,
• Abstract summary: We introduce Video Diffusion for Action Reasoning (Vidar)<n>We pre-train the video diffusion model on 750K multi-view videos from three real-world bimanual robot platforms.<n>With only 20 minutes of human demonstrations on an unseen robot platform, Vidar generalizes to unseen tasks and backgrounds with strong semantic understanding.
• Score: 21.424029706788883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bimanual robotic manipulation, which involves the coordinated control of two robotic arms, is foundational for solving challenging tasks. Despite recent progress in general-purpose manipulation, data scarcity and embodiment heterogeneity remain serious obstacles to further scaling up in bimanual settings. In this paper, we introduce Video Diffusion for Action Reasoning (Vidar), a two-stage framework that leverages large-scale, diffusion-based video pre-training and a novel masked inverse dynamics model for action prediction. We pre-train the video diffusion model on 750K multi-view videos from three real-world bimanual robot platforms, utilizing a unified observation space that encodes robot, camera, task, and scene contexts. Our masked inverse dynamics model learns masks to extract action-relevant information from generated trajectories without requiring pixel-level labels, and the masks can effectively generalize to unseen backgrounds. Our experiments demonstrate that with only 20 minutes of human demonstrations on an unseen robot platform (only 1% of typical data requirements), Vidar generalizes to unseen tasks and backgrounds with strong semantic understanding, surpassing state-of-the-art methods. Our findings highlight the potential of video foundation models, coupled with masked action prediction, to enable scalable and generalizable robotic manipulation in diverse real-world settings.

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