Papers
13
Total Citations
98
H-Index
6
About
Yoonseon Oh is a robotics researcher whose work lies at the intersection of autonomous decision-making, path planning, and human-robot interaction. Her key research areas include probabilistic reasoning under uncertainty, temporal logic-based mission planning, and the integration of large language models (LLMs) into robotic task planning. Oh's most impactful contribution is her work on "Multi-Object Search using Object-Oriented POMDPs" (37 citations), which addresses the critical challenge of enabling robots to reason about multiple objects under uncertainty—a fundamental capability for real-world deployment. She has also made significant advances in robust path planning, developing chance-constrained algorithms that ensure safety and mission success even in the presence of noise and disturbances, as evidenced by her papers on multilayered sampling-based planning for temporal logic missions (15 citations). Her recent work on leveraging LLMs for long-horizon cooking tasks (2024) demonstrates her forward-looking approach to combining symbolic reasoning with modern AI. With a total of over 90 citations across her publications, Oh's research is shaping how robots can operate reliably in uncertain, complex environments—from target tracking to household manipulation.
Research Focus
Key Achievements
Top Papers
- 1Multi-Object Search using Object-Oriented POMDPs37 citations · 2019
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- 3Chance-constrained target tracking for mobile robots8 citations · 2015
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- 7Smartphone-Controlled Telerobotic Systems5 citations · 2014
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