Jae-Moon Chung

Papers

Total Citations

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About

Jae-Moon Chung’s research lies at the intersection of robotic manipulation and active vision, with a focus on enabling robots to grasp objects with human-like perception and reasoning. His work centers on two key areas: volumetric shape reasoning for grasping and anthropomorphic binocular vision planning. In his most cited paper, “Reasoning simplified volumetric shapes for robotic grasping” (2002, 5 citations), Chung introduced a data-driven method that uses global object information—position, orientation, and geometry—to simplify shape reasoning, a crucial step for successful robotic grasping. Earlier, in “Binocular vision planning with anthropomorphic features for grasping parts by robots” (1996, 4 citations), he pioneered the use of binocularity, foveas, and gaze control to mimic human vision, allowing robots to accurately determine an object’s pose for grasping. His follow-up work (2002, 3 citations) refined this approach, detailing viewer-oriented strategies that integrate vision and manipulation. Though his citation counts are modest, Chung’s contributions are notable for their early integration of anthropomorphic principles into robotic systems, laying groundwork for more intuitive human-robot interaction. His research remains relevant for students exploring sensor planning and data-driven grasping in robotics.