mlsanityData to 14 Oct 2026

CUREE: A Curious Underwater Robot for Ecosystem Exploration

arXiv 2303.03943 ↗First posted 7 Mar 2023cs.RO cs.AI cs.CVcited by 13

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Abstract (arXiv)

The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE's ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

5 of 27 references.

References · 5

  1. 2018Fast Online Object Tracking and Segmentation: A Unifying Approachcited by 272
  2. 2023Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehiclescited by 16
  3. 2022SiamMask: A Framework for Fast Online Object Tracking and Segmentationcited by 13
  4. 2020Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstaclescited by 11
  5. 2019Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environmentscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 6.

13 papers.

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  1. 2024SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Modelcited by 36
  2. 2025The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefscited by 9
  3. 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehiclescited by 6
  4. 2026OceanSplat: Object-aware Gaussian Splatting with Trinocular View Consistency for Underwater Scene Reconstructioncited by 2
  5. 2023Robot Goes Fishing: Rapid, High-Resolution Biological Hotspot Mapping in Coral Reefs with Vision-Guided Autonomous Underwater Vehiclescited by 1
  6. 2024Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observationscited by 0
  7. 2025Benchmarking Online Object Trackers for Underwater Robot Position Locking Applicationscited by 0
  8. 2025Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Dockingcited by 0
  9. 2026ReefMapGS: Enabling Large-Scale Underwater Reconstruction by Closing the Loop Between Multimodal SLAM and Gaussian Splattingcited by 0
  10. 2026Semantically-Aware Diver Activity Recognition Framework for Effective Underwater Multi-Human-Robot Collaborationcited by 0
  11. 2026Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learningcited by 0
  12. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehiclescited by 0
1 more
  1. 2026CoralscapesV2: Panoptic and Fine-Grained Visual Scene Understanding in Coral Reefscited by 0