Risk-Sensitive Planning

Latest papers

24 papers6 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Reward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games
Oct 19, 2026Durgam Latha, Dion Reji, Dorde Zikelic +1Partially Observable Stochastic Games Robust Partially Observable Markov Decision Processes
Indian Institute of Technology Bombay Mumbai, India · Nanyang Technological University Singapore, Singapore
Beyond Expected Values: Risk-Sensitive Planning with Distributional Monte-Carlo Tree Search
Oct 16, 2026Quynh Trinh, Trung Nguyen, Nam Nguyen +1Risk-Sensitive Planning Monte Carlo Tree Search
Hanoi University of Science and Technology, Hanoi, Vietnam · Center for AI Research, VinUniversity, Hanoi, Vietnam
Belief Space Planning for Risk Signal Temporal Logic via Mixed-Integer Convex Programming
Oct 12, 2026Shaohang Han, Joris Verhagen, Kaier Liang +1Belief-Space Planning STL Planning
Division of Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · Digital Futures
Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation
Oct 7, 2026Richie R. Suganda, Bin HuRisk-Aware Path Planning Safe Motion Planning
Department of Electrical Engineering and Computer University of Houston Houston, TX 77004, USA · Department of Engineering Technology University of Houston Houston, TX 77004, USA
Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form
Oct 5, 2026Aleš Kučera, Karel ZimmermannUncertainty Quantification Safe Motion Planning
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
RiskFly: Frustum-Aligned Spatio-Temporal Risk Fields for One-Stage Agile Flight in Dynamic Clutter
Oct 3, 2026Luxia Ai, Haopeng Chen, Yuchao Mei +2UAV Navigation Safe Motion Planning
State Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
Sep 26, 2026Yaacov Pariente, Vadim IndelmanRisk-Sensitive Planning Partially Observable Markov Decision Processes
Faculty of Mathematics Technion – Israel Institute of Technology Haifa, Israel · Stephen B. Klein Faculty of Aerospace Engineering; Faculty of Data and Decision Sciences Technion – Israel Institute of Technology Haifa, Israel
When Information is Worth the Risk: Behavioral Valuation for Hazardous Robotic Exploration
Sep 9, 2026Alkesh K. Srivastava, Aamodh Suresh, Carlos Nieto-Granda +1Value of Information Informative Path Planning
Department of Mechanical Engineering, Temple University, Philadelphia, PA 19122, USA · U.S. DEVCOM Army Research Laboratory, Adelphi, MD 20783, USA · Lobster Robotics, The Hague, South Holland, Netherlands
From Uncertainty to Clinical Risk: Severity-Aware Conformal Planning for Interactive Medical Diagnosis
Aug 28, 2026Yue Zhou, Haiyang Zhou, Jin Zhang +4Risk-Sensitive Planning Medical Diagnosis
Lanzhou University, China · Hunan University, China · National University of Singapore, Singapore +2
Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making
Aug 18, 2026Deep Kumar Ganguly, Jan KretinskyRisk-Sensitive Planning Robust RL
Technical University of Munich, Germany · Masaryk University, Brno, Czech Republic
Risk-Aware Kinodynamic Motion Planning Under Uncertainty For Safe Navigation on Planetary Environments
Aug 11, 2026Sachin Sunil Kelkar, Tanmay Dokania, Yashwanth Kumar NakkaSampling-Based Motion Planning Kinodynamic Motion Planning
Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, Atlanta, GA, 30332 USA.
Long-Term Sequential Decision Making under Risk
Jul 22, 2026Irmaan, Mirzanejad, Nadjet Bourdache +1Markov Decision Processes Dynamic Programming
Mohammad · Université de Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, France
Stochastic Multi-Objective Kinodynamic Planning Against Adversaries
Jul 21, 2026Thomas Marshall Vielmetti, Daniel Cherenson, Dimitra PanagouConstrained Motion Planning Kinodynamic Motion Planning
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Robotics and Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Risk-Aware General-Utility Markov Decision Processes
Jul 10, 2026Pedro P. Santos, Fábio Vital, Alberto Sardinha +1Markov Decision Processes Markov Models
INESC-ID · Instituto Superior Técnico, University of Lisbon · Pontifical Catholic University of Rio de Janeiro
RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance
Jun 26, 2026Shiqiang GongDistributional RL Diffusion-Based Planning
Northwestern Polytechnical University
Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving
Jun 2, 2026Chengzhen Meng, Pei Liu, Zhiyu Huang +2Diffusion-Based Planning Risk-Sensitive Planning
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology, Guangzhou, China, and also the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095 USA · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798
RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
May 25, 2026Zhengye Han, Quanyan ZhuSafe Robot Navigation Risk-Aware Path Planning
Department of Electrical and Computer Engineering New York University
MC-Risk: Multi-Component Risk Fields for Risk Identification and Motion Planning
May 20, 2026Maximilian Link, Yingjie Xu, Yingbai Hu +1Safe Motion Planning Autonomous Driving Planning
Technical University of Munich · Munich, Germany · The Chinese University of Hong Kong +3
Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability
May 14, 2026Yushen Liu, Yin-Jen Chen, Ziyi Chen +4Safety-Critical Control Partially Observable RL
University of Virginia · Google · University of Maryland, College Park +3
NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
May 13, 2026Yingming Mao, Ximeng Liu, Jingyi Cheng +8Stochastic Optimization Neural Network Optimization
Xi’an Jiaotong University · Shanghai Innovation Institute · Shanghai Jiao Tong University +2
MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning
May 8, 2026Yusuf Syed, Viraj Parimi, Brian WilliamsContrastive Learning Risk-Sensitive Planning
Massachusetts Institute of Technology
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
Department of Electrical & Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, USA
UMBRELLA: Uncertainty-aware Multi-robot Reactive Coordination under Dynamic Temporal Logic Tasks
Mar 26, 2026Qisheng Zhao, Meng Guo, Hengxuan Du +2Risk-Sensitive Planning Multi-Robot Coordination
School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China · Automatic Control Laboratory, ETH Zürich, Zürich 8092, Switzerland
Multimodal Belief-Space Covariance Steering with Active Probing and Influence for Interactive Driving
Feb 16, 2026Devodita Chakravarty, John Dolan, Yiwei LyuRisk-Sensitive Planning Autonomous Driving Planning
Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India · Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

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

Reward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games

Durgam Latha, Dion Reji, Dorde Zikelic, Shankaranarayanan Krishna

Indian Institute of Technology Bombay Mumbai, India · Nanyang Technological University Singapore, Singapore

Partially Observable Stochastic Games Robust Partially Observable Markov Decision Processes Robust Markov Decision Processes Partially Observable Markov Decision Processes Risk-Sensitive Planning Risk-Sensitive RL

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Robust partially observable Markov decision processes model agents that act under partial observability while adversarial environment resolves uncertainty in the transition probabilities. Reward objectives such as discounted-sum, long-run average and cumulative reward under finite and infinite horizons allow quantifying the utility of an agent. We consider the problem of solving RPOMDPs under all four reward objectives, where the goal is to decide whether the value of a risk measure with respect to a reward objective exceeds a given threshold. We consider different risk measures, such as (1)~expected value, (2)~mean-variance (MV), (3)~conditional value-at-risk (CVaR), and (4)~entropic risk (ERisk). Our results can be summarized as follows. First, we formalize the class of law-invariant risk measures, that subsume all four risk measures above. Second, we propose a unified framework for solving RPOMDPs with discounted-sum, long-run average and cumulative reward objectives under any law-invariant risk measure, by reducing the problem to solving partially observable stochastic games (POSGs). Our unified framework applies to (s,a)-rectangular RPOMDPs with polytopic uncertainty sets, which we show to be {\em equivalent} to POSGs, for each of the four reward objectives and any law-invariant risk measure. We prove equivalence by designing logspace reductions between the RPOMDP and POSG models, that map strategies to strategies and preserve the distribution of the reward objective, up to a constant rescaling factor. Third, as a corollary, we derive new computational complexity results for R(PO)MDPs and POSGs. In particular, we show that solving (s,a)-rectangular RMDPs is as hard as solving simple stochastic games.