Younggil Cho
Papers
4
Total Citations
84
H-Index
3
About
Younggil Cho is a roboticist whose research lies at the intersection of manipulation planning, motion planning, and reinforcement learning, with a focus on enabling robots to operate effectively in cluttered and constrained environments. His most influential work, "Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments" (2019, 60 citations), introduces a novel algorithm that allows a robotic manipulator to systematically relocate obstacles to grasp a target object when no collision-free path exists. This contribution directly addresses a fundamental challenge in robotic manipulation: retrieving objects from densely packed, confined spaces. Cho further advanced this line of inquiry with "Planning for target retrieval using a robotic manipulator in cluttered and occluded environments" (2019, 11 citations), which develops planning strategies for high-density object configurations. Demonstrating versatility, he also explored adaptive locomotion in "Adaptation to environmental change using reinforcement learning for robotic salamander" (2019, 10 citations), applying reinforcement learning to enable a bio-inspired robot to adjust its gait in response to changing terrains. Collectively, Cho’s work has garnered over 80 citations, establishing him as a contributor to practical, real-world robotic autonomy in complex, space-constrained settings.
Research Focus
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Top Papers
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