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About

Gilwoo Lee is a roboticist whose research spans manipulation planning, robot-assisted feeding, and human-robot interaction. Her most influential work, "Hierarchical planning for multi-contact non-prehensile manipulation" (50 citations), introduced a three-stage hierarchical approach to planning sequences of non-prehensile and prehensile actions, enabling robots to manipulate objects without grasping them—a critical capability for cluttered environments. Lee has made significant contributions to assistive robotics, particularly through her work on robot-assisted feeding. Her 2019 and 2022 papers (13 and 19 citations, respectively) developed generalization strategies for skewering diverse food items on a plate, addressing the challenge of varying physical properties in real-world scenarios. She also advanced mobile manipulation with a system integrating architecture, algorithms, and experiments for multi-step tasks (2017, 9 citations). More recently, Lee explored Bayesian reinforcement learning, proposing "Bayesian Residual Policy Optimization" (2021, 4 citations) to enable robust decision-making under uncertainty. Her study of reaching motions for collaborative human-robot interaction (2020, 4 citations) further demonstrates her commitment to safe, adaptive robotics. With a career focused on bridging planning, learning, and real-world deployment, Lee’s work continues to shape how robots interact with complex, unstructured environments.