Papers
14
Total Citations
1,390
H-Index
9
About
Nare Karapetyan is a robotics researcher whose work spans autonomous navigation, state estimation, and multi-robot systems, with particular expertise in underwater and outdoor environments. She has made significant contributions to the field of marine robotics, most notably through her highly cited work on visual-inertial state estimation in underwater domains — her 2019 IROS paper has accumulated over 1,079 citations — which critically evaluated how algorithms developed for indoor and urban settings perform under the unique challenges of aquatic environments. Her 2019 experimental comparison of open-source visual-inertial algorithms underwater (101 citations) further established her as a leading voice in benchmarking robotic perception for non-standard domains. Karapetyan has also advanced multi-robot coverage planning, proposing efficient strategies for coordinating teams of robots across known environments (97 citations), and later extending this to heterogeneous UAV-UGV systems with energy constraints. Her more recent research explores imitation learning and reinforcement learning for stable outdoor and underwater navigation, as well as simulation tools like OysterSim and ChatSim to support environmental monitoring applications. Collectively, her work bridges fundamental robotics challenges with impactful real-world applications in marine conservation and autonomous field robotics.
Research Focus
Key Achievements
Top Papers
- 12019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)1,079 citations · 2019
- 2
- 3Efficient multi-robot coverage of a known environment97 citations · 2017
- 4
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- 6OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring15 citations · 2022
- 7
- 8ChatSim: Underwater Simulation with Natural Language Prompting12 citations · 2023
- 9External Force Field Modeling for Autonomous Surface Vehicles9 citations · 2020
- 10