Max Robotics — Global Service Robot Directory + US Certification

About

Hogun Kee is a roboticist specializing in intelligent grasping and manipulation for cluttered, confined environments. His research focuses on enabling robots to autonomously retrieve target objects in spaces with limited observation and approach angles—a critical challenge for real-world applications like warehouse retrieval and home assistance. Kee’s major contributions include developing a hierarchical 6-DoF grasp detection method that selects optimal approaching directions (7 citations), and pioneering the use of Monte Carlo Tree Search for grasp planning in lateral-access, occluded scenes (5 citations). He also advanced online learning for robotics with a Shannon entropy-regularized neural contextual bandit algorithm (2 citations), which balances exploration and exploitation to improve grasping success. His work on object rearrangement planning for target retrieval in confined spaces (2 citations) further demonstrates his ability to solve complex, multi-step manipulation tasks. With a growing citation record, Kee’s research bridges reinforcement learning, motion planning, and perception, offering practical solutions for robots operating in human-centric, space-constrained environments.