Papers
11
Total Citations
211
H-Index
7
About
Jinhwi Lee is a robotics researcher specializing in task and motion planning (TAMP) for robotic manipulation in cluttered and confined environments. His work addresses one of the most challenging problems in autonomous robotics: how a robotic manipulator can efficiently retrieve a target object when surrounded by densely packed obstacles that block any direct path. Lee's most influential contribution, "Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments" (2019, 60 citations), introduced a foundational algorithm for collision-free obstacle relocation during grasping tasks. Building on this, his 2020 paper "Where to Relocate?" (40 citations) tackled the underexplored question of *where* displaced objects should be placed within the workspace — not just which ones to move. His TAMP framework for fast and resilient manipulation planning (2020, 39 citations) further advanced the field by improving both speed and robustness in real-world cluttered scenarios. Later work expanded his scope to include non-prehensile manipulation strategies and deep reinforcement learning approaches, such as using deep Q-networks for obstacle rearrangement. Collectively accumulating over 200 citations, Lee's research has meaningfully shaped modern approaches to robotic manipulation planning in unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 3Fast and resilient manipulation planning for target retrieval in clutter39 citations · 2020
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- 8Retrieving objects from clutter using a mobile robotic manipulator4 citations · 2019
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- 10Fast and resilient manipulation planning for target retrieval in clutter3 citations · 2020