V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
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| Format: | Preprint |
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2025
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| author | Assran, Mido Bardes, Adrien Fan, David Garrido, Quentin Howes, Russell Mojtaba Komeili Muckley, Matthew Rizvi, Ammar Roberts, Claire Sinha, Koustuv Zholus, Artem Arnaud, Sergio Gejji, Abha Martin, Ada Hogan, Francois Robert Dugas, Daniel Bojanowski, Piotr Khalidov, Vasil Labatut, Patrick Massa, Francisco Szafraniec, Marc Krishnakumar, Kapil Li, Yong Ma, Xiaodong Chandar, Sarath Meier, Franziska LeCun, Yann Rabbat, Michael Ballas, Nicolas |
| author_facet | Assran, Mido Bardes, Adrien Fan, David Garrido, Quentin Howes, Russell Mojtaba Komeili Muckley, Matthew Rizvi, Ammar Roberts, Claire Sinha, Koustuv Zholus, Artem Arnaud, Sergio Gejji, Abha Martin, Ada Hogan, Francois Robert Dugas, Daniel Bojanowski, Piotr Khalidov, Vasil Labatut, Patrick Massa, Francisco Szafraniec, Marc Krishnakumar, Kapil Li, Yong Ma, Xiaodong Chandar, Sarath Meier, Franziska LeCun, Yann Rabbat, Michael Ballas, Nicolas |
| contents | A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning Assran, Mido Bardes, Adrien Fan, David Garrido, Quentin Howes, Russell Mojtaba Komeili Muckley, Matthew Rizvi, Ammar Roberts, Claire Sinha, Koustuv Zholus, Artem Arnaud, Sergio Gejji, Abha Martin, Ada Hogan, Francois Robert Dugas, Daniel Bojanowski, Piotr Khalidov, Vasil Labatut, Patrick Massa, Francisco Szafraniec, Marc Krishnakumar, Kapil Li, Yong Ma, Xiaodong Chandar, Sarath Meier, Franziska LeCun, Yann Rabbat, Michael Ballas, Nicolas Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Robotics A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world. |
| title | V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2506.09985 |