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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2603.04553 |
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| _version_ | 1866911485960126464 |
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| author | Daniel, Tal Qi, Carl Haramati, Dan Zadeh, Amir Li, Chuan Tamar, Aviv Pathak, Deepak Held, David |
| author_facet | Daniel, Tal Qi, Carl Haramati, Dan Zadeh, Amir Li, Chuan Tamar, Aviv Pathak, Deepak Held, David |
| contents | We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. Our architecture is trained end-to-end purely from videos and supports flexible conditioning on actions, language, and image goals. LPWM models stochastic particle dynamics via a novel latent action module and achieves state-of-the-art results on diverse real-world and synthetic datasets. Beyond stochastic video modeling, LPWM is readily applicable to decision-making, including goal-conditioned imitation learning, as we demonstrate in the paper. Code, data, pre-trained models and video rollouts are available: https://taldatech.github.io/lpwm-web |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04553 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling Daniel, Tal Qi, Carl Haramati, Dan Zadeh, Amir Li, Chuan Tamar, Aviv Pathak, Deepak Held, David Machine Learning We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. Our architecture is trained end-to-end purely from videos and supports flexible conditioning on actions, language, and image goals. LPWM models stochastic particle dynamics via a novel latent action module and achieves state-of-the-art results on diverse real-world and synthetic datasets. Beyond stochastic video modeling, LPWM is readily applicable to decision-making, including goal-conditioned imitation learning, as we demonstrate in the paper. Code, data, pre-trained models and video rollouts are available: https://taldatech.github.io/lpwm-web |
| title | Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.04553 |