[2603.04553] Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling

Computer Science > Machine Learning arXiv:2603.04553 (cs) [Submitted on 4 Mar 2026] Title:Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling Authors:Tal Daniel, Carl Qi, Dan Haramati, Amir Zadeh, Chuan Li, Aviv Tamar, Deepak Pathak, David Held View a PDF of the paper titled Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling, by Tal Daniel and 7 other authors View PDF HTML (experimental) Abstract: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: this https URL Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.04553 [cs.LG]   (or arXiv:2603.04553v1 [cs.LG] for this version)   https://doi.org/10.4...