Uncertainty-Aware Exploration

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32 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

Uncertainty Aware Quality-Diversity Optimization For Offline Biological Sequence Design
Oct 31, 2026Alona Rimon, Hannah Janmohamed, Maxime Allard +1Quality-Diversity Optimization Uncertainty-Aware Exploration
Imperial College London · Helical
RAVEN: A Unified Active SLAM System for Rapid Autonomous Visual Exploration with Aerial Robots under Localization Uncertainty
Oct 27, 2026Xinyi Chen, Yichen Zhang, Chenxing Jiang +1Simultaneous Localization and Mapping Autonomous Robot Exploration
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Active Reachability Learning for Humanoid Recoverability Monitoring
Oct 23, 2026Zeyuan Feng, Baljeet Singh, Sarthak Ranjeet Kaingade +2Reachability Analysis Humanoid Robot Fall Recovery
Stanford University · Amazon
QuaNAS: Uncertainty-Aware Reuse of Earlier Searches for Query-Efficient Neural Architecture Search
Oct 22, 2026Brama Seta Putra Satria, Dwi Sunaryono, Misbakhul Munir Irfan Subakti +2Neural Architecture Search Uncertainty-Aware Exploration
Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia · Department of Information Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia · Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
MAPLE: Mondrian-Anchored Prior for Learning-free Indoor Exploration
Oct 20, 2026Kelvin Aladum, Nisar AhmedAutonomous Robot Exploration Uncertainty-Aware Exploration
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies
Oct 14, 2026Sajad Marvi, Julian Schmidt, Alessandro Canevaro +2Uncertainty-Aware Exploration Autonomous Driving Planning
Mercedes-Benz AG, Germany. · Department of Computer Science, University of Freiburg, Germany.
What Information Buys in Active Inference
Oct 13, 2026Melih Kandemir, Mustafa Mert ÇelikokRL Exploration Active Inference
Department of Mathematics and Computer Science, University of Southern Denmark
Understanding Hidden Spatial Preferences in Bayesian Optimization
Oct 10, 2026Maria Bånkestad, Jens SjölundBayesian Optimization Uncertainty-Aware Exploration
Deep Epistemic Value Functions for Optimistic Exploration
Sep 28, 2026Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter +1Value Function Estimation RL Exploration
ETH Zürich, Switzerland · Max Planck Institute for Intelligent Systems, Germany
ICMAPE: In-Context Multiagent Pure Exploration
Sep 27, 2026Xinyi Hu, Alessio Russo, Aldo PacchianoRL Exploration Decentralized MARL
Boston University · Broad Institute of MIT and Harvard
Future Information-Directed Sampling for Bayesian Nonstationary Bandits
Sep 27, 2026Yichen Song, Alessio Russo, Aldo PacchianoMulti-Armed Bandits Non-Stationary Bandits
Boston University · Broad Institute of MIT and Harvard
Where World Models Break: Natural-Input Failure Discovery
Aug 23, 2026Zhanpeng Shi, Zi Liang, Rong Feng +3Adversarial Attacks on World Models Uncertainty-Aware Exploration
School of Computer Science, Northwestern Polytechnical University · The Hong Kong Polytechnic University · Department of Data Science, City University of Hong Kong +3
Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments
Aug 22, 2026Khang Luong, Nam Nguyen, Hoang Ta +2Uncertainty-Aware Exploration RL Exploration
Hanoi University of Science and Technology, Hanoi, Vietnam · Quantum AI & Cyber Security Institute, FPT Corporation, Vietnam.
Information-Based Exploration via Random Features for Reinforcement Learning
Jul 20, 2026Waris Radji, Odalric-Ambrym MaillardRL Exploration Uncertainty-Aware Exploration
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRIStAL, F-59000 Lille, France
UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
Jun 17, 2026Mohamed Nabail, Leo Kaixuan Cheng, Jingmin Wang +1Preference-Based RL Model-Based RL
Learning, Embodied Autonomy, and Forecasting (LEAF) Lab, University of Toronto
TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search
Jun 10, 2026Zhuofan Shi, Mingzhe Ma, Lu Wang +8Inference-Time Search Exploration-Exploitation Tradeoff
1Microsoft · 2East China Normal University
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
Jun 8, 2026Kaustubh Mani, Yann Pequignot, Vincent Mai +1Sharpness-Aware Minimization RL Exploration
Universit´e de Montr´eal · Mila - Qu´ebec AI Institute · Universit´e Laval +2
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
May 28, 2026Ahmed Abouelazm, Felix Klingebiel, Philip Schörner +1Reinforcement Learning Risk-Sensitive RL
FZI Research Center for Information Technology, Germany · Karlsruhe Institute of Technology, Germany
Not all uncertainty is alike: volatility, stochasticity, and exploration
May 19, 2026Payam PirayMulti-Armed Bandits Exploration-Exploitation Tradeoff
Department of Psychology, University of Southern California
ScreenSearch: Uncertainty-Aware OS Exploration
May 15, 2026Michael Solodko, Justin WagleComputer-Use Agents Monte Carlo Tree Search
Microsoft
Delightful Exploration
May 13, 2026Ian OsbandMulti-Armed Bandits Value of Information
Google DeepMind
Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
May 12, 2026Youwei Yu, Jionghao Wang, Zhengming Yu +2Robotic Data Collection Robot Navigation
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA · Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
T $^2$ PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning
May 4, 2026Haixin Wang, Hejie Cui, Chenwei Zhang +7RL for LLM Agents Reinforcement Learning
University of California, Los Angeles · 2Amazon.com Inc.
Uncertainty Guided Exploratory Trajectory Optimization for Sampling-Based Model Predictive Control
Apr 13, 2026O. Goktug Poyrazoglu, Yukang Cao, Rahul Moorthy +1Uncertainty-Aware Exploration Trajectory Optimization
University of Minnesota, Minneapolis, MN 55455, USA. · 2The University of Texas at Austin, Austin, TX 78712, USA.
Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
Apr 13, 2026Rongzhe Wei, Ge Shi, Min Cheng +5Uncertainty-Aware Exploration LLM Agent Planning
Georgia Institute of Technology · Amazon
Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration
Mar 23, 2026Zakaria Mhammedi, James CohanUncertainty-Aware Exploration RL Exploration
Google Research, NYC
Telogenesis: Goal Is All U Need
Mar 10, 2026Zhuoran Deng, Yizhi Zhang, Ziyi Zhang +1Uncertainty-Aware Exploration Attention Mechanisms
Independent Research
XIT: Exploration and Exploitation Informed Trees for Active Gas Distribution Mapping in Unknown Environments
Feb 14, 2026Mal Fazliu, Matthew Coombes, Sen Wang +1Informative Path Planning Active Sensing
Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, UK, LE11 3TU · Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, South Kensington Campus, London, UK, SW7 2AZ
NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces
Feb 3, 2026Rong Fu, Chunlei Meng, Haoyu Zhao +2Multi-Objective Optimization Surrogate-Assisted Optimization
Independent Researcher
Learning Soft Robotic Dynamics with Active Exploration
Oct 31, 2025Hehui Zheng, Bhavya Sukhija, Chenhao Li +3Robot Dynamics Learning Robot Dynamics Identification
Soft Robotics Lab, D-MAVT, ETH Zurich, Zurich, Switzerland · ETH AI Center, ETH Zurich, Zurich, Switzerland · Learning & Adaptive Systems Group, D-INFK, ETH Zurich, Zurich, Switzerland

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Uncertainty Aware Quality-Diversity Optimization For Offline Biological Sequence Design

Alona Rimon, Hannah Janmohamed, Maxime Allard, Antoine Cully

Imperial College London · Helical

Quality-Diversity Optimization Uncertainty-Aware Exploration Molecular Generation Protein Design Black-Box Optimization

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Designing sequences such as nucleic acids and proteins with desired properties is a fundamental challenge in biology. Due to the vast, discrete nature of sequence spaces and the high cost of experimental evaluation, offline computational sequence design plays a crucial role in proposing novel sequences before costly wet-lab validation. In practice, it is also essential that proposed designs are diverse to increase the likelihood of downstream experimental success. This motivates methods that balance performance, novelty, and diversity, whilst mitigating against uncertainty inherent to offline optimization. Quality-Diversity optimization (QD) can discover diverse, high-performing solutions, making it well suited to this problem, but its elitist nature makes reliable exploration under uncertainty challenging. To address this, we introduce Prior-guided Offline MAP-Elites (POME), an uncertainty-aware QD framework explicitly adapted to the offline regime. Our method leverages foundation models as priors to guide search toward biologically plausible regions and enable robust exploration under uncertainty. Across three sequence design tasks, POME consistently improves both performance and diversity over prior offline QD methods, and achieves the most reliable balance compared to offline MBO baselines, obtaining the best performance on two tasks and the second-best on the third, while maintaining high diversity and novelty.