Belief-Space Planning

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10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 47

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Generative-Model Predictive Planning for Navigation in Partially Observable Environments
Jun 17, 2026Thomas Quilter, Yifan Zhu, Guorui Quan +2Belief-Space Planning Diffusion-Based Planning
Department of Computer Science Aalto University Espoo, Finland
PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
Jun 14, 2026Wenjing Tang, Xuanjin Jin, Yuan Liu +3Belief-Space Planning Symbolic Planning
Shanghai Jiao Tong University · Shanghai Innovation Institute
Belief-Space Control for Personalized Cancer Treatment via Active Inference
Jun 9, 2026Deniz Sargun, H. Bugra Tulay, C. Emre KoksalBelief-Space Planning Healthcare
Amazon.com Inc. Palo Alto, CA · HP Inc. Denver, CO · The Ohio State University Columbus, OH
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
Jun 3, 2026Yuanchu Liang, Edward Kim, J. Arden Knoll +4Belief-Space Planning Partially Observable Markov Decision Processes
Australian National University, Canberra ACT 2601, Australia. · Rice University, Houston TX 77005, USA.
PatchWorld: Gradient-Free Optimization of Executable World Models
May 29, 2026Jiaxin Bai, Yue Guo, Yifei Dong +13Belief-Space Planning World Model Learning
1Hong Kong Baptist University · 2Independent Researcher · 3HKUST +4
Integrated Exploration-Aware UAV Route Optimization and Path Planning
May 27, 2026Jimin Choi, Grant Stagg, Cameron K. Peterson +1Belief-Space Planning Trajectory Optimization
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA · Department of Electrical Engineering, Brigham Young University, Provo, UT 84602, USA · Department of Aerospace Engineering, Department of Civil and Environmental Engineering, and Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations
May 21, 2026Jonathan Reasoner, Nicola BezzoBelief-Space Planning Multi-Robot Motion Planning
Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA
Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning
May 13, 2026Qinchuan Cheng, Zhantao Gong, Pengzhan Sun +3Belief-Space Planning Long-Horizon Agent Evaluation
Xi’an Jiaotong University · Nankai University · National University of Singapore +1
Multistep Belief Space Dynamics Learning For Risk-Aware Control
May 12, 2026Jason Gibson, Bogdan Vlahov, Patrick Spieler +1Belief-Space Planning Autonomous Driving
Georgia Institute of Technology, Atlanta GA 30313 USA · California Institute of Technology, Pasadena, CA 91125, USA
Ensuring Logic in the Fog: Sound POMDP Synthesis with LTL Objectives
May 12, 2026Can Zhou, Yulong Gao, Pian YuBelief-Space Planning Reward Shaping
Imperial College London, United Kingdom · University College London, United Kingdom
Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
May 9, 2026Zhuoyun Zhong, Seyedali Golestaneh, Constantinos ChamzasBelief-Space Planning Trajectory Optimization
All authors are affiliated with the Department of Robotics Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA 01609, USA
Multi-Environment POMDPs with Finite-Horizon Objectives
May 8, 2026Léonard Brice, Filip Cano, Krishnendu Chatterjee +2Belief-Space Planning Markov Models
Institute of Science and Technology Austria
Optimizing Trajectory-Trees in Belief Space: An Application from Model Predictive Control to Task and Motion Planning
May 3, 2026Camille Phiquepal, Marc ToussaintBelief-Space Planning Model Predictive Control
Learning and Intelligent Systems Lab (LIS), TU Berlin, Germany
Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
Apr 28, 2026Yiwei Shi, Zixing Song, Mengyue Yang +2Belief-Space Planning Reward Hacking
SEMT, University of Bristol, Bristol, UK · LUCAS, Loughborough University, Loughborough, UK
Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty
Apr 28, 2026Clinton Enwerem, Shreya Kalyanaraman, John S. Baras +1Belief-Space Planning Stochastic Optimization
Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control
Apr 27, 2026Daniel Cao, Beixi Du, Andrew Lowitt +3Belief-Space Planning Model Predictive Control
Cornell
Risk-Constrained Belief-Space Optimization for Safe Control under Latent Uncertainty
Apr 4, 2026Clinton Enwerem, John S. Baras, Calin BeltaBelief-Space Planning Safety-Critical Control
Institute for Systems Research, University of Maryland, College Park, MD, USA

Jun 17, 2026, cs.AIJ/K move · Enter open · S save

Generative-Model Predictive Planning for Navigation in Partially Observable Environments

Thomas Quilter, Yifan Zhu, Guorui Quan, Mingfei Sun+1

Department of Computer Science Aalto University Espoo, Finland

Belief-Space Planning Diffusion-Based Planning Robot Navigation Partially Observable Markov Decision Processes

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Navigation in partially observable environments presents a significant challenge for autonomous agents, requiring effective decision-making with limited sensory information in unknown environments. Belief-based methods, particularly those using neural networks to approximate the belief space, often fail to capture the inherent multimodality of belief spaces, especially in high-dimensional cases with perceptual aliasing. While generative models present a compelling alternative, they typically require substantial data or expert demonstrations and lack explicit mechanisms for long-term planning. In this paper, we introduce BeliefDiffusion, a novel framework that combines the benefits of both generation and planning. BeliefDiffusion leverages diffusion models to explicitly characterize multimodal belief distributions and utilizes Model Predictive Control (MPC) to simultaneously plan ahead. It consists of two steps: (1) Imagining plausible environment configurations based on observation history and (2) Planning efficient navigation strategies across an aggregated configurations. Through extensive experiments in synthetic map environments, we demonstrate that BeliefDiffusion significantly outperforms both model-free reinforcement learning baselines and other generative approaches in navigation success rate and path efficiency. Our results validate that explicitly incorporating multimodal belief representations into planning enables more robust navigation in partially observable settings.

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