CategoryMaritime
Overview
Research laboratory at the University of Minnesota focused on underwater robotics, human-robot collaboration, and computer vision. Led by Associate Professor Junaed Sattar, the lab develops autonomous underwater vehicles (AUVs) and AI systems for diver safety monitoring, marine conservation, and aquatic species management. The lab is part of the Minnesota Robotics Institute (MnRI) and is funded by the National Science Foundation (NSF), the Environment and Natural Resources Trust Fund, and NVIDIA.
Verified record
- Verified deployments
- 2 deployments on file
- Active incidents
- None on file
Key facts
Director
Institution
Location
Research areas
Parent institute
Key achievement
Data & sources
Web sources
3
3 sources backing this record.View all →
Models (1)
View all models →Interactive Robotics and Vision Laboratory (IRV Lab) on the deployment map
Where Interactive Robotics and Vision Laboratory (IRV Lab)'s robots are verified operating. Explore the deployment map by place and type.
Relationships
Current leadership (1)
- Junaed Sattar Lab Director & Principal Investigatorsecondary-verified
Safety record
No incidents on record for Interactive Robotics and Vision Laboratory (IRV Lab).
Only active incidents are counted. Retracted incidents are excluded from this summary but remain reachable at their canonical URLs.
Full safety record: incidents, sourcing, and exposure data →
Operated deployments (2)
Operator customers (1)
- Interactive Robotics and Vision Laboratory (IRV Lab)2 deployments
Recent coverage
Interactive Robotics and Vision Laboratory (IRV Lab) in third-party press
Peer companies
- Shihang Intelligent1 model
Recent activity
Every change to this record is dated, sourced, and independently verified where marked.
- Record createdJul 29, 2026
Added to the verified registry
- Deployment recordedJun 1, 2024
MeCO AUV at Lake Superior, Duluth, Minnesota, USA
- Deployment recordedJan 1, 2024
MeCO AUV at Caribbean Sea, Barbados
Sources (3)
- Minnesota Interactive Robotics and Vision Laboratory · https://irvlab.cs.umn.edu/
- AI underwater robots can now track diver stress via exhaled bubbles · https://techxplore.com/news/2026-07-ai-underwater-robots-track-diver.html
- AI-powered underwater robots revolutionize marine conservation · https://www.nsf.gov/news/ai-powered-underwater-robots-revolutionize-marine
Common questions
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Is Interactive Robotics and Vision Laboratory (IRV Lab) safe?
Methodology: Verified · 3 sources (no primary) · last reviewed 2026-07-29
Verification posture
Verified
Low confidence
Review state
Stable
Last reviewed 2026-07-29
Sources by quality tier
- 3
- unclassified
- Unclassified source
The framework is documented at /methodology. Corrections at /corrections. Reviewer: DEPLOY editorial team.
Methodology surface for Interactive Robotics and Vision Laboratory (IRV Lab).Peer companies
- Shihang Intelligent1 model
In the press
Recent coverage mentioning Interactive Robotics and Vision Laboratory (IRV Lab) from third-party publications. Automatically surfaced; not part of the verified registry record.
AI underwater robots can now track diver stress via exhaled bubbles
University of Minnesota IRV Lab develops AI system for underwater robots to monitor diver health by tracking exhaled bubbles, published in International Journal of Robotics…
AI-powered underwater robots revolutionize marine conservation
NSF-funded IRV Lab develops MeCO AUV with AI vision for tracking invasive species in aquatic environments.
AI-powered underwater robots revolutionize marine conservation
University of Minnesota IRV Lab develops AI-powered AUVs for marine conservation and invasive species tracking.
Machine-readable surfaces
- Markdown mirror: /companies/irv-lab.md
- RSS feed: /companies/irv-lab/feed.xml
- JSON-LD: embedded in this page’s head
- REST API: /v1/companies/7338dbfc-f2b6-456b-81e3-7695b715d1ff
- Revision history: /companies/irv-lab/history
- Data documentation: /data
- Query this programmatically: Deploy MCP
Peer companies
- Shihang Intelligent1 model
Video
3D Vision with a SICK IVC 3D and NAtional Instruments LabVIEW
Academic Robot combines with Vision through ORiN's OpenCV Provider to locate the object and pick up. I use two templates with 2 steps angle verification and wi
D. Park, Z. Erickson, T. Bhattacharjee, and C. Kemp. “Multimodal Execution Monitoring for Anomaly Detection During Robot Manipulation”, IEEE International Confe
Pick-and-place operation based on consecutive robotic skills. For more info, please refer to the paper "Using Robot Skills for Flexible Reprogramming of Pick Op
This test set-up is part of my master's thesis regarding Random Bin Picking. It is capable of autonomously detecting, locating, picking and placing E40 light bu
Detection of DUPLO using image processing and a robot to pick them up
We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object. We captured 336 real-world objects with 9 RGB-D