Model-Based Planning

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38 papers in the last four weeks, up 192% on the four weeks before. 0.4% of all new papers.

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Latest papers 166

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CausalDreamer: Learning Predictive World Models with Latent Disentanglement
Oct 8, 2026Prince Jha, Nils Lukas, Kun Zhang +1World Model Learning Causal World Models
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · Carnegie Mellon University
Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation
Oct 7, 2026Tzu-Yu Chuang, Ching-Hsiang Chang, Yi-Hsiu Lee +3Robotic Manipulation Robot Task Planning
National Yang Ming Chiao Tung University · National Tsing Hua University
Model-Based Geometry-Aware Generative Optimization for Constrained Locomotion Planning
Oct 6, 2026Zhilin He, Xinyuan Wang, Changliu LiuConstrained Generative Modeling Constrained Motion Planning
Carnegie Mellon University · University of Michigan, Ann Arbor
Learning Commute-Time-Preserving World Models for Planning
Oct 1, 2026Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch +1Representation Learning World Model Learning
Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland · Faculty of Science, University of Basel, Switzerland
SCOPE-AD: Sequential cost-aware ordinal-belief planning with energy-based models for diagnostic agents
Oct 1, 2026Ziwen Yu, Ivan Koychev, Elizabeth Coulthard +7Cost-Aware Inference Model-Based Planning
University of Bristol, Bristol, UK · Imperial College London, London, UK · Hunan Normal University, Changsha, China +1
Supervise What Decides Success: Criterion-Aligned Auxiliary Losses for Latent World-Model Planning
Oct 1, 2026Takumi Hara, Kanata SuzukiWorld Model Learning World Models for Robotics
Kyoto University Kyoto, Japan · Fujitsu Limited Kanagawa, Japan
In CEM, a World Model Is Also a Proposal Mechanism
Oct 1, 2026Oliver Obst, Frieder StolzenburgBlack-Box Optimization Model-Based Planning
UNSW Sydney · Harz University of Applied Sciences
JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts
Sep 30, 2026Zheyuan Zhang, Suyu Ye, Nakul Agarwal +5Test-Time Adaptation Latent Dynamics Modeling
Honda Research Institute USA · Johns Hopkins University
Social-WM: Safety-Aware Latent World Models for Robot Social Navigation
Sep 30, 2026Zhihao Zheng, Mooi Choo ChuahWorld Models for Robotics World Model-Based Planning
Computer Science and Engineering department, P.C. Rossin College of Engineering and Applied Science, Lehigh University, Bethlehem, PA 18015, USA.
The Planning Limits of Latent World Models
Sep 30, 2026Ali Alrasheed, Basim Azam, Naveed AkhtarWorld Model-Based Planning Action-Conditioned World Models
The University of Melbourne, Australia
DeepJEPA: Scaling World Models from Within
Sep 30, 2026Zijian Jin, Yunbei Zhang, Yuanzhe Liu +5World Model Learning World Model-Based Planning
New York University · Tulane University · UIUC +4
Learning to Plan from Random Exploration
Sep 29, 2026Deqian Kong, Guangyan Sun, Sheng Cheng +6Long-Horizon Planning Model-Based Planning
UCLA · University of Minnesota · Amazon AGI +3
Anisotropic Representations Improve Planning in JEPA World Models
Sep 29, 2026Mingu Kang, Yoori Oh, Sookyung Kim +1Representation Learning World Model-Based Planning
Seoul National University · Ewha Womans University
Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust
Sep 29, 2026Ziqi Wen, Ting Xu, Lianyu Wang +5Uncertainty Quantification World Model-Based Planning
Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore · Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore · Bioengineering Program, Biomedical Sciences Division (BioMed), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia +3
PrecogUI: Proactive GUI Agents via Pre-cognitive Simulation and Experience Retrieval
Sep 29, 2026Bin Kang, Jiarui Ouyang, Li Jiang +2GUI Agents Long-Horizon Agent Evaluation
University of Chinese Academy of Sciences · Harbin Institute of Technology · Shenzhen Loop Area Institute +2
DSWM: Decomposed Spatio-Temporal World Model for Demand-Driven UAV Base Station Repositioning
Sep 29, 2026Shengjie Zhong, Zhongliang Zhao, Jingxuan Chen +4World Model Learning Wireless Communications
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Distinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion
Sep 29, 2026Yibo Guo, Xiaodan WangRobot Failure Recovery Model-Based Planning
School of Computer and Artificial Intelligence, Zhengzhou University, Henan Zhengzhou 450001, China
ATLAS: Aligned Transport of Latent Structure for Reliable World Model Planning
Sep 28, 2026Ke Fang, Yupu Yao, Lu ChengWorld Models World Model-Based Planning
Pennsylvania State University
Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
Sep 28, 2026Antonio Pariente, Ignacio Boero, Nikolai Matni +1Dynamical Systems World Model Learning
University of Pennsylvania
CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism
Sep 28, 2026R. Khorrambakht, Joaquim Ortiz-Haro, Stephan Weiss +13D Gaussian Splatting Model-Based Planning
Machines in Motion Lab, Tandon School of Engineering, New York University · Department of Smart Systems Technologies, University of Klagenfurt, Austria. · Artificial and Natural Intelligence Toulouse Institute (ANITI)
FONDANT: Strong and Best-Effort Planning via Antichains
Sep 28, 2026Benjamin Aminof, Tuan Khai Nguyen, Sasha RubinSymbolic Planning Model-Based Planning
Technical University of Vienna, Austria · School of Computer Science, The University of Sydney, Australia
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Sep 28, 2026Yichao Liang, Amber Li, Dat Nguyen +13World Models for Robotics Residual Dynamics Learning
Basis Research Institute · University of Cambridge · Carnegie Mellon University +6
LRC-JEPA: Disentangling Dynamics and Residual Context for Efficient World Models
Sep 28, 2026Luzhe Huang, Lei Chu, Jingyi Liang +1Disentangled Representation Learning World Models for Robotics
Independent researcher
Beyond One-Step Accuracy: State-Affine Latent Transition for Reliable Visual Planning
Sep 27, 2026Boyuan Zhang, Yingjun Du, Xiantong Zhen +1Dynamical Systems Latent Dynamics Modeling
UCAS-Terminus AI Lab, School of Engineering Science, University of Chinese Academy of Sciences · VIS Lab, University of Amsterdam · Central Research Institute, United Imaging Healthcare, Co., Ltd.
Hamiltonian JEPA: Action-Conditioned World Models with an Inherited Control State
Sep 27, 2026Tamim Zoabi, Ameen Ali, Lior WolfJoint-Embedding Predictive Architecture Model-Based Planning
The Blavatnik School of Computer Science, Tel Aviv University
Adaptive Latent Capacity for World Models
Sep 26, 2026Idan Achituve, Lior Dikstein, Idit Diamant +2World Model Learning Latent Dynamics Modeling
Arm Research Israel
Not All Errors Matter: Decision-Relevant Prediction Error Predicts Planning Quality
Sep 26, 2026Linhao Wang, Yiyan Fan, Dongjin HuangWorld Model-Based Planning Model-Based Planning
Shanghai Film Academy, Shanghai University
Aim Short to Reach Far: Your Frozen World Model Can Plan Better Than You Think
Sep 24, 2026Xvyuan Liu, Jianjie Fang, Wei Wu +2World Models for Robotics World Model-Based Planning
Tsinghua University
Representation World Model: Learning States, Transition and Executable Plans in Representation
Sep 24, 2026Yijun Yuan, Weicheng Zheng, Weibang Wang +7World Models for Robotics World Model-Based Planning
IIIS, Tsinghua University
Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes
Sep 23, 2026Yue Wang, George AtiaRobust Markov Decision Processes Markov Decision Processes
Department of Electrical and Computer Engineering University of Central Florida

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CausalDreamer: Learning Predictive World Models with Latent Disentanglement

Prince Jha, Nils Lukas, Kun Zhang, Salem Lahlou

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · Carnegie Mellon University

World Model Learning Causal World Models Disentangled Representation Learning Model-Based Planning Causal Representation Learning

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World models for control must capture which aspects of the environment respond to the agent's actions and which are relevant to reward. Generative world models such as Dreamer 4 consist of a video tokenizer, which encodes each frame into a latent, and a dynamics model, which is pretrained to predict future latents from past latents and actions. Yet the tokenizer is trained with a reconstruction objective, without action or reward supervision, so its latent provides no explicit mechanism to separate controllable, uncontrollable, reward-relevant, and reward-irrelevant information. We propose \textit{CausalDreamer}, which keeps the tokenizer frozen and re-encodes its latent into a factored representation of four groups along two axes: controllability, where only the two controllable groups receive the action, and reward relevance, learned by predicting the reward from the two reward-relevant groups. The pretrained dynamics model is then fine-tuned to predict the factored representation. We evaluate \textit{CausalDreamer} and the pretrained world model it starts from with model-predictive planning on 20 MMBench2 tasks: 10 clean tasks seen during training and 10 unseen tasks, of which 6 are manipulated variants of clean tasks with a changed background, object, or maze layout, and 4 are new environments. We normalize returns so that a policy taking uniformly random actions scores 0 and an expert scores 1. \textit{CausalDreamer} achieves a 14% higher normalized score than the pretrained world model on the clean tasks (0.199 vs.\ 0.175) and a 25% higher score on the manipulated variants (0.307 vs.\ 0.246), while neither model scores meaningfully above the random policy in the new environments. Additionally, our analysis shows that the factored representation separates reward-irrelevant changes, such as a changed background, from its reward-relevant groups.

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Latest papers 166