Adaptive Sensing

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23 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

Bayesian Sequential Optimal Experimental Design for Single-Pixel Imaging using Reinforcement Learning
Nov 2, 2026Marcos Antonio Obando, Tianyuan Wang, Tanja Tarvainen +2Single-Pixel Imaging Adaptive Sensing
Adaptive search with path-constrained mobile sensors using multi-arm bandits
Oct 29, 2026Abraham P. Vinod, Parth K. Thaker, Gautam Dasarathy +1Adaptive Sensing Multi-Robot Systems
Mitsubishi Electric Research Laboratories, Cambridge, MA, USA · Arizona State University, Tempe, AZ, USA
Lightweight Change Detection for Adaptive Collective Perception in Dynamic Environments
Oct 25, 2026Thiemen Siemensma, Tiago Travassos, Jiacheng Zhang +1Swarm Robotics Cooperative Perception
Faculty of Science and Engineering, University of Groningen, The Netherlands
BAARC: Uncertainty-Driven Adaptive Partial Spectral Measurement to Full Reconstruction for Material Classification
Oct 23, 2026Adithya Ashok Chalain Valapil, Aishwarya Venkataramanan, Joachim DenzlerAdaptive Sensing Raman Spectroscopy
Computer Vision Group, Friedrich Schiller University Jena, 07743, Thuringia, Germany
Vehicle-State-Aware Confidence Estimation for Direct Wind Sensing on a Multirotor
Oct 19, 2026Sri Arjun Senthil Kumar, April Zi Qian Chan, Chen-Hua YeowConfidence Estimation Adaptive Sensing
Pathwise Information Certificates for Decentralized Adaptive Sensing
Oct 7, 2026Theodoros TsiligkaridisAdaptive Sensing Active Sensing
MIT Lincoln Laboratory
Agentic Semantic Sensing for Resource-Adaptive AI-RAN
Oct 6, 2026Zhongqin Wang, Xiaoqi Zhang, Nan Yang +3Adaptive Sensing Active Perception
Global Big Data Technologies Centre, University of Technology Sydney, Sydney, NSW 2007, Australia · School of Science, Engineering and Digital Technologies, University of Southern Queensland, Toowoomba, QLD 4350, Australia · School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW 2006, Australia
SenseAgent: An LLM Agent for Adaptive Cross-Domain IMU Sensing
Sep 25, 2026Tianya Zhao, Chuan Liu, Xuyu WangAdaptive Sensing Wearable Sensing
Florida International University Miami, Florida, USA
Audio-based UAV Localization with Adaptive Temporal Correspondence via Reinforcement Learning
Sep 21, 2026Haoxiang Lei, Mingzheng Feng, Daotong Wang +1Active Perception Robot Localization
How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing
Sep 18, 2026Vincent Corlay, Andriy EnttselActive Perception Optimal Stopping
Mitsubishi Electric R&D Centre Europe
SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants
Sep 1, 2026Rohit Menon, Shiva Rudra Lolla, Niklas Mueller-Goldingen +3Depth Completion Agricultural Robotics
Humanoid Robots Lab, University of Bonn, Germany · Center for Robotics, Bonn, Germany · Institute of Geodesy and Geoinformation, University of Bonn +1
CARE: Camera-Residual Reserves for First Sightings in Adaptive LiDAR Sensing
Aug 25, 2026Jiachen Gong, Yun Li, Ehsan Javanmardi +2Adaptive Sensing Autonomous Driving
The University of Tokyo, Tokyo, Japan · National Institute of Informatics, Tokyo, Japan
FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition
Aug 13, 2026Zhou Chen, Sathyanarayanan N. AakurNext-Best-View Planning Active Sensing
CSSE Department Auburn University, Auburn, AL, 36849
Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification
Jun 21, 2026Farbod Siahkali, Mengxue Hou, Vijay GuptaConstrained Motion Planning Trajectory Optimization
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906, USA · Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
Policy-based Foveated Imaging and Perception
Jun 1, 2026Howard Xiao, Jan Ackermann, Boyang Deng +1Active Perception Policy Learning
Stanford University, USA
Active Sensing Subserves Task-Level Control
May 21, 2026Andrew Lamperski, Debojyoti Biswas, Eric S. Fortune +3Active Sensing Feedback Control
Department of Electrical and Computer Engineering, University of Minnesota · Laboratory for Computational Sensing and Robotics, Johns Hopkins University · 3Federated Department of Biological Sciences, New Jersey Institute of Technology +3
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
May 19, 2026Sanggeon Yun, Ryozo Masukawa, Minhyoung Na +5Efficient Multimodal Inference Edge Computing
University of California, Irvine Irvine, CA, USA · Kookmin University Seoul, South Korea · Shibaura Institute of Technology Saitama, Japan
Active Learning for Conditional Generative Compressed Sensing
May 6, 2026Alexander DeLise, Nick DexterCompressed Sensing Conditional Generative Modeling
Department of Scientific Computing Department of Mathematics Florida State University · Department of Scientific Computing Florida State University
[Emerging Ideas] Artificial Tripartite Intelligence: A Bio-Inspired, Sensor-First Architecture for Physical AI
Apr 15, 2026You Rim Choi, Subeom Park, Hyung-Sin KimAdaptive Sensing Embodied AI
Seoul National University, Seoul, South Korea
PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning
Mar 26, 2026Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin AlamdariContextual RL Adaptive Sensing
Department of Civil and Environmental Engineering Florida State University, Tallahassee, FL, USA
ADAPT: Adaptive Dual-projection Architecture for Perceptive Traversal
Mar 17, 2026Shuo Shao, Tianchen Huang, Wei Gao +1Humanoid Robot Locomotion Terrain-Aware Robot Locomotion
Department of Automation, University of Science and Technology of China, Anhui 230026, China. · Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China.
Adaptive Sensing of Continuous Physical Systems for Machine Learning
Mar 4, 2026Felix Köster, Atsushi UchidaAdaptive Sensing Active Perception
Department of Information and Computer Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama, 338–8570, Japan
Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training
Feb 26, 2026Aheli Saha, René Schuster, Didier StrickerAdaptive Sensing Event-Based Vision
Deutsches Forschungszentrum für Künstliche Intelligenz, Kaiserslautern, Germany

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Bayesian Sequential Optimal Experimental Design for Single-Pixel Imaging using Reinforcement Learning

Marcos Antonio Obando, Tianyuan Wang, Tanja Tarvainen, Tristan van Leeuwen+1

Single-Pixel Imaging Adaptive Sensing Bayesian Experimental Design Computational Imaging Compressed Sensing Bayesian RL

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Adaptive compressed sensing (CS) aims to select the most informative measurements sequentially, which is crucial for many modern imaging systems. This task can be formulated as a Bayesian sequential optimal experimental design (sOED) problem, where each new acquisition is chosen to maximally reduce uncertainty about the unknown image given prior information and subsequent data provided by acquisitions. However, in single-pixel imaging (SPI), the large design space consists of all possible binary illumination patterns. Directly optimizing the Bayesian sOED objective over this space using conventional methods is generally challenging. In this work, we reformulate sOED for SPI as a reinforcement learning (RL) problem. In RL, an agent learns a policy through repeated experimental trials to maximize a reward, here given as the expected information gain, the most complete measure of uncertainty reduction in Bayesian OED. This avoids the need to directly optimize complex information-theoretic objectives during a measurement to obtain the next design. Instead, the result is a learned policy function that directly maps the current posterior belief about the image to the next optimal binary measurement pattern, in a computationally efficient way. Using a simple yet representative SPI imaging scenario, this work evaluates the scalability of the learned policy under two different policy parameterizations. Furthermore, we investigate how performance deteriorates when only approximate posterior information is available or the measurements are impaired by noise, and propose several strategies to introduce additional design constraints motivated by biomedical applications. The numerical experiments demonstrate that the proposed Bayesian sOED-RL framework remains robust and can be extended to more complex, large-scale SPI scenarios.