Embodied World Models

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20 papers in the last four weeks, up 186% on the four weeks before. 0.2% of all new papers.

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From World Models to World Action Models: A Concise Tutorial for Robotics
Jul 1, 2026Xiaoxiong Zhang, Xiong Zeng, Wei ZhangWorld Model Learning World Models for Robotics
School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China · LimX Dynamics, Shenzhen, China
Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
Jul 1, 2026Jinwoo Jang, Daniel J. Rho, Sihyung Yoon +2World Model Learning World Models
Sungkyunkwan University · Corresponding author
DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation
Jun 30, 2026Ziyu Shan, Zhenyu Wu, Xiaofeng Wang +2Video Generation Robotic Manipulation
Nanyang Technological University, Singapore · Beijing University of Posts and Telecommunications, Beijing, China · GigaAI
Orca: The World is in Your Mind
Jun 29, 2026Yihao Wang, Yuheng Ji, Mingyu Cao +54World Model Learning Multimodal Pretraining
Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation
Jun 29, 2026Hong Chen, Daqi Liu, Zehan Zhang +10Visual Navigation World Model-Based Planning
Huazhong University of Science and Technology, Wuhan, China · Xiaomi EV, Beijing, China · Zhejiang University, Hangzhou, China
ViPSim: Collaborating Visual and Parameter Spaces for Consistent Long-Horizon Embodied World Models
Jun 27, 2026Longyu Chen, Heng Li, Wei Yang +2Action-Conditioned Video Generation World Models for Robotics
Huawei Technologies Co., Ltd. · Huazhong University of Science and Technology
PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation
Jun 26, 2026Peiwen Zhang, Yufan Deng, Shangkun Sun +11Physical Consistency in Video Generation Video Diffusion Models
Peking University, NVIDIA
iFLYTEK-Embodied-Omni Technical Report
Jun 24, 2026Yuan Zhang, Jingfei Ni, Guanchen Lu +12Unified Multimodal Models Robot Motion Generation
1iFLYTEK · University of Science and Technology of China · 2LindenBot
Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models
Jun 23, 2026Xin Wang, Wenxuan Liu, Tongtong Feng +1Video Generation Controllable Video Generation
Department of Computer Science and Technology, Tsinghua University, Haidian District, Beijing, 100084, China · Beijing National Research Center for Information Science and Technology, Haidian District, Beijing, 100084, China
SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments
Jun 21, 2026Yundaichuan Zhan, Minghe Gao, Zhongqi Yue +7Symbolic Planning World Models
Zhejiang University, Hangzhou, China · Chalmers University of Technology, Gothenburg, Sweden · Lanzhou University, Lanzhou, China
Self-Evolving Cognitive Framework via Causal World Modeling for Embodied Scientific Intelligence
Jun 21, 2026Yi Yu, Tetsunari InamuraCausal World Models Embodied AI
Graduate School of Advanced Science and Engineering, Hiroshima University, Japan · Advanced Intelligence and Robotics Research Center, Brain Science Institute, Tamagawa University, Japan
World Action Models: A Survey
Jun 18, 2026Qiuhong Shen, Shihua Zhang, Yue Liao +5Action-Conditioned Video Generation Action-Conditioned World Models
National University of Singapore
Reward as An Agent for Embodied World Models
Jun 18, 2026Pu Li, Zhigang Lin, Qiang Wu +3Reward Modeling Reward Hacking
Kairos Team
ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
Jun 17, 2026Yuyang Zhang, Wenyao Zhang, Zekun Qi +7Action-Conditioned World Models World Action Models
Shanghai Jiao Tong University · Tsinghua University · Tencent Robotics X +1
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Jun 15, 2026Jie Zhang, Xiaoyue Chen, Anzhe Chen +36Action-Conditioned Video Generation Video Diffusion Models
Qwen Team
Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI
Jun 15, 2026Kairos Team, Fei Wang, Shan You +21Efficient Inference Action-Conditioned World Models
Kairos Team
SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model
Jun 15, 2026Kai Tang, Peidong Jia, Zhong Chu +15Constrained RL Robot Safety
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Beijing Innovation Center of Humanoid Robotics · Nanyang Technological University +2
How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position
Jun 13, 2026Yang Yu, Shiyuan Zhang, Yifei Sheng +2Model-Based Planning World Model Evaluation
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · Cirquar Technologies, Nanjing, China
EWAM: An Enhanced World Action Model for Closed-Loop Online Adaptation in Embodied Intelligence
Jun 10, 2026Xin Zhou, Cong MiaoRobotic Control Adaptive Inference
Astronex Robotics · Nanjing University of Information Science and Technology
iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
Jun 8, 2026Zhenyu Wu, Xiuwei Xu, Yukun Zhou +8Representation Learning Cross-Embodiment Robot Learning
Beijing University of Posts and Telecommunications · Tsinghua University · GigaAI +1
AnchorWorld: Embodied Egocentric World Simulation with View-based Evolution Customization
Jun 5, 2026Yu Li, Menghan Xia, Gongye Liu +8World Models for Robotics World Models
1Tsinghua University · ∗This work was conducted during the author’s internship at Kling Team, Kuaishou Technology. · 2HUST +3
CAPE: Contrastive Action-conditioned Parallel Encoding for Embodied Planning
Jun 5, 2026Cong Chen, Haowen Wang, Zhixiang Zhang +2Contrastive Learning Model-Based Planning
Robots Need More than VLA and World Models
Jun 4, 2026Elis Karcini, Faisal Mehrban, Quang Nguyen +6Robot Foundation Models Cross-Embodiment Robot Learning
Stanford University · Istituto Italiano di Tecnologia · ETH Zurich +2
Towards a Data Flywheel for Embodied Intelligence in Logistics
Jun 4, 2026Anlan Yu, Zaishu Chen, Zhiqing Hong +1Synthetic Data Generation for Robotics Robotics
Peking University Beijing, China · JD Logistics Beijing, China · HKUST (Guangzhou) Guangzhou, China
PiL-World: A Chunk-Wise World Model for VLA Policy-in-the-Loop Evaluation
Jun 4, 2026Chong Ma, Taiyi Su, Jian Zhu +4Action-Conditioned World Models Robotic Policy Evaluation
1Tongji University · 2AIRC, Midea Group
Flash-WAM: Modality-Aware Distillation for World Action Models
Jun 3, 2026Arman Akbari, Ci Zhang, Arash Akbari +6Video Diffusion Models Action-Conditioned World Models
1Northeastern University · University of Georgia · 3EmbodyX Inc.
MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
Jun 3, 2026Zhichao Yang, Yuanze Hu, Haojie Hao +7Mobile GUI Automation Latent Visual Reasoning
Beihang University · Northwestern Polytechnical University · Institute of Software, Chinese Academy of Sciences +2
A 3D Isovist World Model -- Revealing a City's Unseen Geometry and Its Emergent Cross-City Signature
Jun 2, 2026Xuhui Lin, Stephen Law, Nanjiang Chen +2Representation Learning World Model Learning
Department of Geography University College London, UK · School of Project Management, Faculty of Engineering The University of Sydney, AU · School of Engineering Cardiff University, UK +1
Cosmos 3: Omnimodal World Models for Physical AI
Jun 1, 2026NVIDIA, :, Aditi +293Unified Multimodal Models World Models
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Jun 1, 2026Junjie Ye, Rong Xue, Basile Van Hoorick +6Robot Skill Learning Robotic Data Collection
1USC Physical Superintelligence (PSI) Lab · 2Toyota Research Institute

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From World Models to World Action Models: A Concise Tutorial for Robotics

Xiaoxiong Zhang, Xiong Zeng, Wei Zhang

School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China · LimX Dynamics, Shenzhen, China

World Model Learning World Models for Robotics Action-Conditioned World Models World Action Models Embodied World Models

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World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains ambiguous across communities. This tutorial presents a design-space view of world models as action-conditioned predictive models that estimate the future evolution of task-relevant observations or states. We categorize existing methods into observation-space and state-space world models, comparing their trade-offs in visual fidelity, spatial structure, physical interpretability, and control usability. We further introduce world action models, which connect predicted futures with executable robot actions, and summarize four representative paradigms: imagine-then-execute, video-feature-conditioned action prediction, joint video-action modeling, and auxiliary video prediction for policy learning. The goal of this tutorial is to clarify the conceptual scope of world (action) models and provide a structured taxonomy for embodied prediction and control.

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