Latent-Space Planning

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31 papers13 papers in the last four weeks, up 225% on the four weeks before. 0.1% of all new papers.

Learning Goal-Conditioned Subgoals through Frozen Hierarchical World Models
Oct 19, 2026Xiang Kaibo, Liu QingfengWorld Model-Based Planning World Models
Department of Industrial and Systems Engineering, Hosei University
Vector-Quantized World Model: Latent Feedback Motion Planning for Robot Manipulation
Oct 19, 2026Xuezhou Xu, Chung-Pang Wang, Zhenzhen Li +1Long-Horizon Robotic Manipulation World Model-Based Planning
New York University, New York, NY, USA. · NVIDIA.
Latent World Models Should Plan with Distances Along the Latent Manifold
Oct 18, 2026Zunzhi You, Linwei Tao, Siyu Xu +2Latent World Models Latent-Space Planning
The University of Sydney · Machine Learning Research, Apple
Action-Aligned Latent Planning for Versatile Humanoid Loco-Manipulation
Oct 17, 2026Yutang Lin, Guanya Shi, C. Karen LiuHumanoid Loco-Manipulation Latent-Space Planning
Amazon FAR. · Carnegie Mellon University. · Stanford University.
Beyond Action Prediction: Learning Search-Free Planning in Latent Space
Oct 16, 2026Guanchen Du, Jinhao Du, Yanpeng Sun +3Latent-Space Planning Model-Based Planning
Nanjing University of Science and Technology · University of Oxford · Singapore University of Technology and Design +1
Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models
Oct 13, 2026Junlin Wu, Wen Fan, Dandan ZhangLatent-Space Planning World Model-Based Planning
Imperial College London, London, United Kingdom
Sampling-based Kinodynamic Planning with World Models
Oct 9, 2026Yitian Gao, Hanlan Yang, Zachary KingstonLatent-Space Planning Motion Planning
Department of Computer Science, Purdue University
LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild
Oct 8, 2026Linkai Liu, Yuntian Zhang, Zhenshan Bing +5Robot Navigation Latent World Models
Nanjing University · Imperial College London · Sony AI +1
H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning
Oct 5, 2026Wancong Zhang, Basile Terver, Michael Rabbat +2World Models for Robotics World Models
NYU · Advanced Machine Intelligence · INRIA Paris +1
How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models
Oct 4, 2026Lama Moukheiber, Haotian Xue, Yongxin ChenWorld Model-Based Planning Long-Horizon Planning
Georgia Institute of Technology
Learning Commute-Time-Preserving World Models for Planning
Oct 1, 2026Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch +1Spectral Graph Embedding Representation Learning
Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland · Faculty of Science, University of Basel, Switzerland
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
Control-Geometry Straightening for Sampling-Based Latent Planning
Sep 28, 2026Ziang Fu, Ning NingRepresentation Learning World Models for Robotics
Texas A&M University
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
Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals
Sep 25, 2026Royson Lee, Fady Rezk, Titouan Parcollet +2Long-Horizon Planning Hierarchical Planning
Samsung AI, Cambridge, UK · University of Edinburgh, UK
LePlanner: An Iterative Amortized Controller For World Models
Sep 12, 2026Saksham Bansal, Om Naphade, Chayan Aggarwal +1World Model-Based Planning Model-Based Planning
Department of Mechanical Engineering, Indian Institute of Technology Roorkee · Department of Physics, Indian Institute of Technology Roorkee · Department of Mathematics, Indian Institute of Technology Roorkee +1
Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning
Sep 3, 2026Muyuan Liu, Yue Huang, Zheng Liang +1World Models for Robotics World Model-Based Planning
GENISOM AI, Beijing, China
Hydra: A Navigation World Action Model with Discrete Latent Planning and Continuous Flow-Matching Execution
Aug 29, 2026Mohammad Nazeri, Alexandyr Card, Samira Huber +6Kinodynamic Motion Planning World Models for Robotics
George Mason University · Kiel University · Ludwig Maximilian University Munich +1
LeFlow: Generative Latent Flow Planning for World Models
Aug 25, 2026Hsiang-Wei Huang, Jianxu Shangguan, Junbin Lu +1Latent-Space Planning World Model-Based Planning
University of Washington, United States
Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost
Aug 14, 2026Xiaodi Huang, Ziyi Ding, Jingtian Wan +6World Model-Based Planning World Model Learning
Institute of Automation, Chinese Academy of Sciences · Shanghai Jiao Tong University · Tsinghua Shenzhen International Graduate School +4
The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use
Aug 13, 2026Joyjeet SinghWorld Model-Based Planning Model-Based Planning
Independent researcher
Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control
Jul 28, 2026Jiaxin Bai, Jiaxuan XiongWorld Model-Based Planning Joint-Embedding Predictive Architecture
Hong Kong Baptist University Hong Kong, China
AdaJEPA: An Adaptive Latent World Model
Jun 30, 2026Ying Wang, Oumayma Bounou, Yann LeCun +1Continual Test-Time Adaptation World Model-Based Planning
New York University · AMI Labs
LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields
Jun 30, 2026Felipe Tommaselli, Francisco Affonso, Arthur Pompeu +4Robot Navigation Agricultural Robotics
Mobile Robotics Group, Center for Robotics (CRob), São Carlos School of Engineering (EESC), University of São Paulo, São Carlos, SP, Brazil · DASLab, Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, IL, USA
Motion Planning in Compressed Representation Spaces
Jun 29, 2026Lukas Lao Beyer, Sertac KaramanMotion Planning Autonomous Driving Planning
MIT LIDS.
IMWM: Intuition Models Complement World Models for Latent Planning
Jun 1, 2026Baoqi Gao, Ruize Han, Miao Wang +1World Model-Based Planning AI Control
Beihang University · Shenzhen University of Advanced Technology
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
May 13, 2026Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9End-to-End Autonomous Driving Vision-Language-Action Models
Qualcomm AI Research · Qualcomm Technologies, Inc
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
World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems
Apr 16, 2026Runze Li, Hongyin Zhang, Junxi Jin +5Vision-Language-Action Models Model-Based Planning
Westlake University Hangzhou, China · Nanjing University Suzhou Campus, 1520 Taihu Avenue, Suzhou, China
Reasoning as Energy Minimization over Structured Latent Trajectories
Mar 30, 2026David K. JohanssonLatent-Space Planning Continuous Latent Reasoning
Polished Snow Inc.

Oct 19, 2026J/K move · Enter open · S save

Learning Goal-Conditioned Subgoals through Frozen Hierarchical World Models

Xiang Kaibo, Liu Qingfeng

Department of Industrial and Systems Engineering, Hosei University

World Model-Based Planning World Models Latent-Space Planning Efficient Inference

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Hierarchical world models plan over long horizons by passing predicted states from a coarse model to a lower-level controller. Choosing these states can be expensive: the upper-level planner repeatedly differentiates through candidate macro-action trajectories. We replace this search with a lightweight goal-conditioned network trained through the frozen high-level predictor. The network supplies a conditioning vector, while the predictor generates the next latent waypoint. Training uses hindsight goals and state-prediction losses on offline trajectories, without search-solution targets or macro-action labels. Inference retains an autoregressive high-level rollout, but requires no high-level candidate optimization. We evaluate this approach on the shared-latent HWM checkpoints distributed with H-JEPA, using three backbone seeds and three head initializations per backbone. With a common evaluation protocol, mean success is 70.4% on Cube, 48.7% on Push-T, 100.0% on FourRoom, and 71.1% on Visual AntMaze. The method improves over the corresponding HWM search baseline on three environments and has the same mean on Push-T. Dedicated measurements on an NVIDIA L40S give 1.28-4.39x end-to-end speedups for batched evaluation. Ablations identify state-adaptive conditioning as a consistent contributor, while predictor-mediated subgoals and unrestricted conditions have environment-dependent benefits.