Junhong Min

Papers

Total Citations

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About

Junhong Min is a robotics researcher whose work centers on advancing robotic manipulation in complex, real-world environments, with a particular focus on 6-DoF grasping, object pose estimation, and bin-picking automation. His most cited paper, "Hierarchical 6-DoF Grasping with Approaching Direction Selection" (2020, 7 citations), addresses a critical challenge in robot grasping by introducing a novel approach that selects optimal approaching directions for 6-DoF grasps in cluttered scenes, moving beyond traditional point-cloud-based methods. This work has laid important groundwork for more robust and practical robotic grasping systems. More recently, Min's 2024 paper "Sim-to-real Object Pose Estimation for Random Bin Picking" (4 citations) tackles the industrial challenge of random bin picking, where accurate instance segmentation and pose estimation from 3D point clouds are essential. By addressing the sim-to-real gap, this work enables learning-based methods to perform effectively without extensive real-world supervision. Min's research bridges the gap between simulation and real-world deployment, making significant strides toward fully autonomous industrial robotics. His contributions are particularly valuable for students and researchers interested in practical robotic grasping, perception, and manipulation in unstructured environments.