Active Sensing

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

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TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning
May 8, 2026Giacomo SpiglerActive Vision for Robot Manipulation Active Sensing
Department of Intelligent Systems Tilburg University Netherlands
Active Contact Sensing for Robust Robot-to-Human Object Handover
May 6, 2026Linfeng Li, Lin Shao, David HsuActive Sensing Robotic Manipulation
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
An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics
Apr 25, 2026Kaixian Qu, Han Wang, Victor Klemm +2Active Perception Mobile Robotics
Robotic Systems Lab, ETH Zurich
Fast Neural-Network Approximation of Active Target Search Under Uncertainty
Apr 24, 2026Bilal Yousuf, Zsofia Lendek, Lucian BusoniuActive Sensing
Technical University of Cluj-Napoca, Romania · Corresponding Member of Romanian Academy
Paparazzo: Active Mapping of Moving 3D Objects
Apr 21, 2026Davide Allegro, Shiyao Li, Stefano Ghidoni +1Next-Best-View Planning Informative Path Planning
University of Padova · 2LIGM, ´Ecole Nationale des Ponts et Chauss´ees, IP Paris, Univ Gustave Eiffel, CNRS
LASER: Learning Active Sensing for Continuum Field Reconstruction
Apr 21, 2026Huayu Deng, Jinghui Zhong, Xiangming Zhu +2Active Sensing
MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science, Shanghai Jiao Tong University.
Active World-Model with 4D-informed Retrieval for Exploration and Awareness
Apr 17, 2026Elaheh Vaezpour, Amirhosein Javadi, Tara JavidiActive Perception World Model Learning
KavAI · UCSD
I-Perceive: A Foundation Model for Vision-Language Active Perception
Feb 28, 2026Yongxi Huang, Zhuohang Wang, Wenjing Tang +3Active Perception Robot Foundation Models
Shanghai Jiao Tong University · Shanghai Innovation Institute · Beihang University
Adaptive Time Series Reasoning via Segment Selection
Feb 20, 2026Shvat Messica, Jiawen Zhang, Kevin Li +2Time Series Reasoning Time Series QA
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA · The Hong Kong University of Science and Technology (Guangzhou) · Massachusetts Institute of Technology +1
Schur-MI: Fast Mutual Information for Robotic Information Gathering
Feb 12, 2026Kalvik Jakkala, Jason O'Kane, Srinivas AkellaRobotic Data Collection Informative Path Planning
Texas A&M University · University of North Carolina at Charlotte
LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception
Dec 9, 2025Simon de Moreau, Andrei Bursuc, Hafid El-Idrissi +1Autonomous Driving Perception Active Sensing
Valeo - Mines Paris PSL · Valeo · Mines Paris PSL
Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping
Date pendingJiahui Zhang, Gongbo Liang, Yu ZhangNext-Best-View Planning World Models for Robotics
Boise State University · Texas A&M University–San Antonio

May 8, 2026, cs.ROJ/K move · Enter open · S save

TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning

Giacomo Spigler

Department of Intelligent Systems Tilburg University Netherlands

Active Vision for Robot Manipulation Active Sensing Robotic Manipulation Robot Imitation Learning Robot Manipulation Benchmarks Active View Selection+1

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Active vision -- where a policy controls its own gaze during manipulation -- has emerged as a key capability for imitation learning, with multiple independent systems demonstrating its benefits in the past year. Yet there is no shared benchmark to compare approaches or quantify what active vision contributes, on which task types, and under what conditions. We introduce TAVIS, evaluation infrastructure for active-vision imitation learning, with two complementary task suites -- TAVIS-Head (5 tasks, global search via pan/tilt necks) and TAVIS-Hands (3 tasks, local occlusion via wrist cameras) -- on two humanoid torso embodiments (GR1T2, Reachy2), built on IsaacLab. TAVIS provides three evaluation primitives: a paired headcam-vs-fixedcam protocol on identical demonstrations; GALT (Gaze-Action Lead Time), a novel metric grounded in cognitive science and HRI that quantifies anticipatory gaze in learned policies; and procedural ID/OOD splits. Baseline experiments with Diffusion Policy and

π_0

reveal that (i) active vision generally helps, consistently across training runs, but benefits are task-conditional rather than uniform; (ii) multi-task policies degrade sharply under controlled distribution shifts on both suites; and (iii) imitation alone yields anticipatory gaze that lands on the task-relevant object, with lead times comparable to those of the human demonstrations, although head motion is less smooth than in the demonstrations, an effect of action chunking that success rate does not reveal. Code and evaluation scripts are released at https://github.com/spiglerg/tavis; demonstrations (LeRobot v3.0; ~2200 episodes) and trained baselines at https://huggingface.co/tavis-benchmark.

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