Underwater Robotics

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23 papers in the last four weeks, up 360% on the four weeks before. 0.2% of all new papers.

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Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling
Mar 10, 2026Eric Chen, Travis Manderson, Nare Karapetyan +3Underwater Robotics Active Visual Perception
Massachusetts Institute of Technology and Woods Hole Oceanographic Institution · Massachusetts Institute of Technology · Woods Hole Oceanographic Institution +1
Unified Structural-Hydrodynamic Modeling of Underwater Underactuated Mechanisms and Soft Robots
Mar 9, 2026Chenrui Zhang, Yiyuan Zhang, Yunfei Ye +3Underwater Robotics System Identification
Department of Mechanical Engineering and Advanced Robotics Centre, National University of Singapore, Singapore. · Singapore-MIT Alliance for Research and Technology (SMART) Centre, 138602, Singapore.
Marinarium: A Modular Experimental Facility for Reproducible Maritime and Space-Analog Field Robotics
Feb 26, 2026Ignacio Torroba, David Dorner, Victor Nan Fernandez-Ayala +14Space Robotics Robotics
Division of Aerospace, Moveability and Naval Architecture, Royal Institute of Technology, 10044 Stockholm, Sweden · Department of Decision and Control Systems, Royal Institute of Technology, 10044 Stockholm, Sweden · Department of Robotics, Perception and Learning, Royal Institute of Technology, 10044 Stockholm, Sweden +1
SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM
Jan 15, 2026Onur Bagoren, Seth Isaacson, Sacchin Sundar +6Simultaneous Localization and Mapping Metric Depth Estimation
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes
Dec 9, 2025Lauritz Rismark Fosso, Herman Biørn Amundsen, Marios Xanthidis +1RL Control Sim-to-Real Transfer
SINTEF Ocean, Trondheim, Norway
A Collaborative Reasoning Framework for Anomaly Diagnostics in Underwater Robotics
Nov 4, 2025Markus Buchholz, Niamh Ellis, Rahaf Abu Hara +2Robotics Human-in-the-Loop AI
School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK
Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots
Oct 28, 2025Hongxu Zhao, Guangyang Zeng, Yunling Shao +2Underwater Robotics
The School of Data Science, Chinese University of Hong Kong, Shenzhen, Shenzhen, P. R. China.

Mar 10, 2026, cs.ROJ/K move · Enter open · S save

Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling

Eric Chen, Travis Manderson, Nare Karapetyan, Peter Edmunds+2

Massachusetts Institute of Technology and Woods Hole Oceanographic Institution · Massachusetts Institute of Technology · Woods Hole Oceanographic Institution+1

Underwater Robotics Active Visual Perception

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Autonomous underwater vehicles (AUVs) are increasingly used to survey coral reefs, yet efficiently locating specific coral species of interest remains difficult: target species are often sparsely distributed across the reef, and an AUV with limited battery life cannot afford to search everywhere. When detections of the target itself are too sparse to provide directional guidance, the robot benefits from an additional signal to decide where to look next. We propose using the visual environmental context -- the habitat features that tend to co-occur with a target species -- as that signal. Because context features are spatially denser and often vary more smoothly than target detections, we hypothesize that a reward function targeted at broader environmental context will enable adaptive planners to make better decisions on where to go next, even in regions where no target has yet been observed. Starting from a single labeled image, our method uses patch-level DINOv2 embeddings to perform one-shot detections of both the target species and its surrounding context online. We validate our approach using real imagery collected by an AUV at two reef sites in St. John, U.S. Virgin Islands, simulating the robot's motion offline. Our results demonstrate that one-shot detection combined with adaptive context modeling enables efficient autonomous surveying, sampling up to 75

\%

of the target in roughly half the time required by exhaustive coverage when the target is sparsely distributed, and outperforming search strategies that only use target detections.

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