mlsanityData to 7 Oct 2026

Assessing Vision-Language Models for Perception in Autonomous Underwater Robotic Software

arXiv 2602.10655 ↗First posted 11 Feb 2026cs.SE cs.ROcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Autonomous Underwater Robots (AURs) operate in challenging underwater environments, including low visibility and harsh water conditions. Such conditions present challenges for software engineers developing perception modules for the AUR software. To successfully carry out these tasks, deep learning has been incorporated into the AUR software to support its operations. However, the unique challenges of underwater environments pose difficulties for deep learning models, which often rely on labeled data that is scarce and noisy. This may undermine the trustworthiness of AUR software that relies on perception modules. Vision-Language Models (VLMs) offer promising solutions for AUR software as they generalize to unseen objects and remain robust in noisy conditions by inferring information from contextual cues. Despite this potential, their performance and uncertainty in underwater environments remain understudied from a software engineering perspective. Motivated by the needs of an industrial partner in assurance and risk management for maritime systems to assess the potential use of VLMs in this context, we present an empirical evaluation of VLM-based perception modules within the AUR software. We assess their ability to detect underwater trash by computing performance, uncertainty, and their relationship, to enable software engineers to select appropriate VLMs for their AUR software.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 21 of its 42 references that are in the map. First posted on arXiv 11 Feb 2026.

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21 of 42 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis3 Feb

Other references · 20

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
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8 more
  1. 2020TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debriscited by 45
  2. 2024Uncertainty-Aware Evaluation for Vision-Language Modelscited by 28
  3. 2023MarineGPT: Unlocking Secrets of Ocean to the Publiccited by 23
  4. 2025Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robotscited by 17
  5. 2024Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysiscited by 3
  6. 2025UnderwaterVLA: Dual-brain Vision-Language-Action architecture for Autonomous Underwater Navigationcited by 3
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  8. 2025USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robotscited by 2

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  1. 2025OceanGym: A Benchmark Environment for Underwater Embodied Agents
  2. 2025DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring
  3. 2025A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models
  4. 2025UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding
  5. 2026CORAL: COntextual Reasoning And Local Planning in A Hierarchical VLM Framework for Underwater Monitoring
  6. 2023Deep Learning for Visual Navigation of Underwater Robots
  7. 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Models
  8. 2026Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap