Object-Centric World Models

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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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World-as-Graph: Relational World Modeling Through Latent Space Graphs
Sep 30, 2026Yaqi Yang, Shuo Huang, Yujin Huang +3Graph Structure Learning World Model Learning
School of Computing Technologies, RMIT University, Australia · Faculty of Information Technology, Monash University, Australia · KStelrix Star Dynamics Lab, KStelrix, China +1
From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Sep 28, 2026Bangjun Wang, Longyan Wu, Yukun Wei +8Object-Centric World Models Robotic Tool Use
The University of Hong Kong · Shanghai Innovation Institute · Xiaomi EV
Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation
Sep 2, 2026Param Thakkar, Parsika Paresh Shah, Manisha Sushant GoteWorld Model Learning World Models for Robotics
Veermata Jijabai Technological Institute · Arizona State University · ZuiGO Private Limited
Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
Aug 12, 2026Shukrullo Nazirjonov, Sai Prasanna, Anna Manasyan +1World Models World Model-Based Planning
University of Tuebingen · Max Planck Institute for Intelligent Systems
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Jul 6, 2026Hongyu Li, Wanjia Fu, Xiaoyan Cong +11World Models Deformable Object Manipulation
Brown University, Providence, RI · Columbia University, New York, NY · Massachusetts Institute of Technology, Cambridge, MA
Object-Centric Environment Modeling for Agentic Tasks
Jul 3, 2026Yiyang Li, Tianyi Ma, Zehong Wang +2Continual Learning for LLM Agents World Models
University of Notre Dame · †Corresponding Author
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3
Jul 1, 2026David Courtis, Wenhao Li, Scott SannerWorld Model Learning World Models
Department of Computer Science, University of Toronto
FlowMo-WM: A World Model with Object Momentum and Hidden Ambient Drift
Jun 11, 2026Yitao Jiang, Luyang Zhao, Muhao Chen +1World Model Learning World Models for Robotics
Department of Computer Science Dartmouth College, Hanover, NH · Department of Electrical and Computer Engineering Clemson University, Clemson, SC · Department of Mechanical and Aerospace Engineering University of Houston, Houston, TX
MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
Jun 11, 2026Hanyang Yu, Haitao Lin, Jingbo Zhang +4World Model Learning Language-Conditioned Robot Manipulation
1The Hong Kong University of Science and Technology · ‡Work done during an internship at Tencent Robotics X · 2Tencent Robotics X +1
Slots, Transitions, Loops: Learning Composable World Models for ARC
Jun 10, 2026Gege Gao, Bernhard Schölkopf, Andreas GeigerWorld Model Learning Visual Reasoning
University of Tübingen · ETH Zürich
Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
Jun 7, 2026Raktim Gautam Goswami, Prashanth Krishnamurthy, Yann LeCun +1Long-Horizon Robotic Manipulation World Models for Robotics
Tandon School of Engineering, New York University · Courant Institute of Mathematical Sciences, New York University · AMI Labs
Learning Action-Conditional and Object-Centric Gaussian Splatting World Models for Rigid Objects
Jun 1, 2026Jens U. Kreber, Lukas Mack, Joerg Stueckler3D Gaussian Splatting Dynamical Systems
Intelligent Perception in Technical Systems Group University of Augsburg Augsburg, Germany
WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models
May 24, 2026Bohai Gu, Taiyi Wu, Yueyang Yuan +9Camera-Controlled Video Generation Object-Centric World Models
The Hong Kong University of Science and Technology · AI Technology Center, Tencent Video, Tencent · Wuhan University +1
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
May 7, 2026Yushan Liu, Peibo Sun, Shoujie Li +7World Models for Robotics Action-Conditioned World Models
Tsinghua University · Shanghai Jiao Tong University · Nanyang Technological University
Soft Geometric Inductive Bias for Object Centric Dynamics
Dec 17, 2025Hampus Linander, Conor Heins, Alexander Tschantz +2World Model Learning World Models
VERSES AI · University of Sussex, Department of Informatics

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

World-as-Graph: Relational World Modeling Through Latent Space Graphs

Yaqi Yang, Shuo Huang, Yujin Huang, Fucai Ke+2

School of Computing Technologies, RMIT University, Australia · Faculty of Information Technology, Monash University, Australia · KStelrix Star Dynamics Lab, KStelrix, China+1

Graph Structure Learning World Model Learning World Models Object-Centric World Models Object-Centric Representation Learning Predictive Representation Learning

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World models aim to learn representations of real-world environments and predict their future evolution. Recent object-centric world models have made expressive progress by representing visual scenes as sets of object-level latent states, but object-object relations are often captured only implicitly, which limits explicit relational and temporal structure modeling and object-centric dynamic memory modeling. To address such challenges, we propose World-As-Graph (WAG), a graph-based object-centric world model that introduces relational inductive bias into JEPA-style predictive representation learning. The proposed WAG contains two main modules: (1) Relation-aware structure induction, which constructs time-varying latent graphs from object-centric slots and designs relation-aware object masking policies to guide relational object representation learning in latent space; (2) Object-centric memory transition, which maintains and updates object-level dynamic states by combining relational information from neighboring objects with historical memory, enabling effective autoregressive future prediction. Extensive experiments on both visual reasoning and robotic manipulation tasks could demonstrate the superior performance of our proposed WAG.

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