Minjae Kang

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About

Minjae Kang is a robotics researcher advancing autonomous navigation and manipulation in complex, human-centered environments. His work centers on socially-aware navigation and object rearrangement, particularly in crowded or confined spaces where traditional planning fails. Kang’s most cited paper, “SCAN: Socially-Aware Navigation Using Monte Carlo Tree Search” (2023, 10 citations), introduces a global planner that integrates pedestrian comfort into pathfinding, enabling robots to move through crowds without causing discomfort—a critical step toward seamless human-robot coexistence. He further tackles the challenge of retrieving occluded objects in lateral-access environments, such as cluttered shelves or tight compartments. In “Grasp Planning for Occluded Objects in a Confined Space with Lateral View” (2022, 5 citations), Kang uses Monte Carlo Tree Search to plan safe, collision-free grasping while accounting for limited observation angles and surrounding obstacles. His follow-up work, “Object Rearrangement Planning for Target Retrieval in a Confined Space with Lateral View” (2023, 2 citations), extends this to full rearrangement tasks, where robots must relocate objects to extract a target. By combining tree-search optimization with real-world constraints, Kang’s research directly impacts service robotics, warehouse automation, and assistive technologies, offering practical solutions for robots operating in dynamic, space-limited settings.