Papers
21
Total Citations
362
H-Index
9
About
Inho Lee is a leading roboticist whose research centers on humanoid locomotion, whole-body control, and environmental perception for legged robots operating in complex, real-world settings. He is best known for his pivotal role on Team KAIST during the DARPA Robotics Challenge (DRC) Finals, where his work on the DRC-HUBO+ robot system and control strategy—detailed in a highly cited 2016 paper (127 citations)—helped the team navigate disaster-response tasks under degraded communication. Lee’s major contributions include a robust walking controller that optimizes ankle, hip, and stepping strategies for push recovery (68 citations), and a walking-wheeling dual-mode strategy that allows humanoids to adapt their locomotion to terrain. He has also advanced perception for legged robots, developing algorithms to detect usable planar regions for stable walking (25 citations) and a collision detection system using oriented bounding boxes for real-time safety (19 citations). More recently, Lee has explored anomaly detection in industrial robots using graph convolutional networks and variational autoencoders (2024). His work bridges theoretical control and practical deployment, making him a key figure in humanoid robotics for disaster response and autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 3Detecting Usable Planar Regions for Legged Robot Locomotion25 citations · 2020
- 4Walking-wheeling dual mode strategy for humanoid robot, DRC-HUBO+20 citations · 2016
- 5Collision detection system for the practical use of the humanoid robot19 citations · 2015
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- 10Footstep Planning for Autonomous Walking Over Rough Terrain7 citations · 2019