Papers
12
Total Citations
256
H-Index
9
About
Jaesik Choi is a leading researcher in robotics and artificial intelligence, whose work bridges the critical gap between safe navigation, multi-robot coordination, and explainable AI. His foundational research on safe mobile robot navigation, considering environmental visibility (85 citations), established key principles for robots operating in dynamic, human-populated spaces. Choi has made significant contributions to multi-robot systems, notably developing a novel reinforcement learning approach for cooperative task allocation that overcomes the scalability limitations of traditional methods. His work on combining high-level planning with motion planning for robotic manipulation has been instrumental in enabling general-purpose physical tasks. A hallmark of Choi's research is his pioneering work on lifted relational inference, extending Kalman filtering and variational methods to handle large-scale, hybrid continuous-discrete models—a breakthrough with applications from robotics to environmental engineering. More recently, he has advanced the field of explainable robotics, creating adaptive, hierarchical deep reinforcement learning systems that not only deploy navigation skills robustly but also provide transparency into their decision-making, addressing a critical need for reliable and trustworthy autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Safe Navigation of a Mobile Robot Considering Visibility of Environment85 citations · 2009
- 2Cooperative Multi-Robot Task Allocation with Reinforcement Learning40 citations · 2021
- 3Combining planning and motion planning39 citations · 2009
- 4Lifted Relational Kalman Filtering26 citations · 2013
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- 7Lifted Relational Variational Inference11 citations · 2012
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- 9Factor-guided motion planning for a robot arm9 citations · 2007
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