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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Latent Goal Prediction from Language for Model-Based Planning
May 26, 2026Samuel Barbeau, Simon Roy, Giovanni Beltrame +2World Model-Based Planning Long-Horizon Planning
1École de Technologie Supérieure, Montréal · 2International Laboratory on Learning Systems (ILLS) · 3Polytechnique Montréal +4
When Does LeJEPA Learn a World Model?
May 25, 2026David Klindt, Yann LeCun, Randall BalestrieroWorld Model Learning Representation Identifiability
Cold Spring Harbor Laboratory · New York University · Brown University
Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations
May 25, 2026Minghao Fu, Fan Feng, Nicklas Hansen +1World Model Learning World Models
University of California, San Diego
Drift-Resistant Navigation World Model with Anchored Epipolar Guidance
May 23, 2026Po-Chien Luan, Zimin Xia, Wuyang Li +2World Models for Robotics Model-Based Planning
Can Predicted Dynamics Exist in the Physical World?
May 23, 2026Barak OrRobotic Control Model-Based Planning
STATE16 Founder and CEO, STATE16
Neuro-Inspired Inverse Learning for Planning and Control
May 22, 2026Maryna Kapitonova, Tonio BallRobot Policy Learning Model-Based Planning
NeuroMentum AI · IMBIT, University of Freiburg, Germany
World Model Control by Trajectory Reachability Metrics
May 21, 2026Liangyu Li, Shengzhi Wang, Libin Qiu +2Reachability Analysis World Models for Robotics
Tongji University
Efficient Agentic Reasoning Through Self-Regulated Simulative Planning
May 21, 2026Mingkai Deng, Jinyu Hou, Lara Sá Neves +4LLM Planning World Model-Based Planning
Carnegie Mellon University · Institute of Foundation Models (IFM)
Visual Sculpting: Visually-Aligned Planning Representations for Long-Horizon Robot Clay Sculpting
May 17, 2026Peter Schaldenbrand, Jean OhLong-Horizon Robotic Manipulation Deformable Object Manipulation
1The Robotics Institute, Carnegie Mellon University · 2Lavoro AI Research
Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
May 16, 2026Vivienne Huiling Wang, Tinghuai Wang, Joni PajarinenWorld Model-Based Planning Hierarchical RL
Department of Electrical Engineering and Automation, Aalto University, Finland · Qutwo, Finland
Imperfect World Models are Exploitable
May 15, 2026Logan Mondal Bhamidipaty, Esmeralda S. Whitammer, David Abel +2Reward Hacking World Model-Based Planning
University of Edinburgh · Stanford University
Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition
May 13, 2026Yaniv Eliyahu Amiri, Noah Chicoine, Jacqueline Griffin +1Computational Cognitive Modeling Clinical Decision-Making
Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA · Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA · Department of Psychology, Northeastern University, Boston, MA, USA
State-Centric Decision Process
May 12, 2026Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun +2Markov Decision Processes Model-Based Planning
University of California, Irvine · 2Northeastern University
Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs
May 12, 2026Joshua Wendland, Markel Zubia, Roman Andriushchenko +6Markov Models Learning with Missing Data
1Ruhr University Bochum · 2Brno University of Technology · 3Radboud University Nijmegen +2
Particle Monte Carlo Tree Search
May 9, 2026Yaniv Oren, Viliam Vadocz, Joery A. de Vries +3Monte Carlo Tree Search Model-Based Planning
Department of Intelligent Systems, TU Delft · Centrum Wiskunde & Informatica, Amsterdam · Department of Computer Science, ETH Zürich +2
Latent Geometry Beyond Search: Amortizing Planning in World Models
May 9, 2026Hoang Nguyen, Xiaohao Xu, Xiaonan HuangWorld Model-Based Planning Model-Based Planning
Department of Robotics, University of Michigan, Ann Arbor
MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning
May 8, 2026Yusuf Syed, Viraj Parimi, Brian WilliamsContrastive Learning Model-Based Planning
Massachusetts Institute of Technology
Model-Driven Policy Optimization in Differentiable Simulators via Stochastic Exploration
May 8, 2026Yuval Aroosh, Ayal TaitlerPolicy Optimization RL Exploration
Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer Sheva, Israel
Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
May 8, 2026Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo +1World Models for Robotics World Model-Based Planning
National Yang Ming Chiao Tung University
Predictive but Not Plannable: RC-aux for Latent World Models
May 8, 2026Wenyuan Li, Guang Li, Keisuke Maeda +2Reachability Analysis World Model-Based Planning
Hokkaido University
See Tomorrow, Act Today: Foresight-Driven Autonomous Driving
May 8, 2026Bozhou Zhang, Nan Song, Yuang Wang +3End-to-End Autonomous Driving World Models
School of Data Science, Fudan University · Shanghai Innovation Institute · Imperial College London +1
When to Trust Imagination: Adaptive Action Execution for World Action Models
May 7, 2026Rui Wang, Yue Zhang, Canyang Chen +3Long-Horizon Robotic Manipulation World Models for Robotics
Southern University of Science and Technology, Shenzhen, China · The University of Hong Kong, Hong Kong, China
HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
May 7, 2026Haoyun Tang, Haodong Cui, Keyao Xu +2World Model Learning Latent Dynamics Modeling
1Xi’an Jiaotong University · 2Huazhong University of Science and Technology · 3Nankai University +1
Using Common Random Numbers for Simulation-based Planning with Rollouts
May 6, 2026Sandarbh Yadav, Frederic J Maliakkal, Harshad Khadilkar +1Monte Carlo Tree Search Model-Based Planning
Department of Computer Science and Engineering, IIT Bombay, India
TRAP: Tail-aware Ranking Attack for World-Model Planning
May 3, 2026Siyuan Duan, Ke Zhang, Xizhao LuoBackdoor Attacks World Model-Based Planning
Department of Computer Science and Technology, Soochow University Suzhou, China
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
May 1, 2026Sen Cui, Jingheng MaLatent Dynamics Modeling World Models
Tsinghua University
Lifting Embodied World Models for Planning and Control
Apr 28, 2026Alex N. Wang, Trevor Darrell, Pavel Izmailov +2Robotic Control World Model-Based Planning
Computer Science, New York University · BAIR, UC Berkeley
Cortex 2.0: Grounding World Models in Real-World Industrial Deployment
Apr 22, 2026Adriana Aida, Walid Amer, Katarina Bankovic +25Long-Horizon Robotic Manipulation World Models for Robotics
Sereact GmbH
Planning in entropy-regularized Markov decision processes and games
Apr 21, 2026Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard +2Value Function Estimation Markov Models
DeepMind Paris · SequeL team, Inria Lille
Scale-free adaptive planning for deterministic dynamics & discounted rewards
Apr 20, 2026Peter L. Bartlett, Victor Gabillon, Jennifer Healey +1Markov Decision Processes Model-Based Planning
University of California, Berkeley, USA · Noah’s Ark Lab, Huawei Technologies, London, UK · Adobe Research, San Jose, USA +1

May 26, 2026, cs.AIJ/K move · Enter open · S save

Latent Goal Prediction from Language for Model-Based Planning

Samuel Barbeau, Simon Roy, Giovanni Beltrame, Christian Desrosiers+1

1École de Technologie Supérieure, Montréal · 2International Laboratory on Learning Systems (ILLS) · 3Polytechnique Montréal+4

World Model-Based Planning Long-Horizon Planning Model-Based Planning Latent World Models

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Planning with world models is bottlenecked by compounding prediction errors and the difficulty of defining optimizable goals. Visual targets provide precise local gradients but poor distant guidance, while language is flexible yet limited by noisy cross-modal alignment or dependence on large generative models unsuited for the high-sampling nature of model-based planning. To address these challenges, we introduce Latent Goal Prediction from Language (LAGO), a framework that predicts both sequences of intermediate goal states from language instructions and action-conditioned rollouts, all within the same latent space. Rather than optimizing toward a single global objective, LAGO dynamically decomposes instructions into explicitly predicted, locally tractable latent subgoals. By updating these subgoals online and using a soft minimum trajectory cost during planning, LAGO enables an agent to follow coherent latent trajectories over long horizons. Evaluation across multiple environments planning horizons shows that LAGO avoids the sharp degradation of prior methods. By achieving robust and precise long-horizon planning purely from language, LAGO bridges the precision of visual goals with the flexibility of text-guided control.

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Latest papers 166