Jun Kwan

Papers

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About

Jun Kwan is an emerging researcher specializing in human-robot interaction, with a particular focus on robotic vision and the seamless transfer of objects between humans and robots. His work addresses one of the fundamental challenges in collaborative robotics: enabling robots to safely and intelligently receive objects from human partners in real-world settings. Kwan's most notable contribution, "Object-Independent Human-to-Robot Handovers Using Real Time Robotic Vision" (2020, 9 citations), introduced a versatile framework that employs a single gripper-mounted RGB-D camera, a generic object detector, and a rapid grasp selection algorithm — making robot handovers practical across a wide variety of objects without specialized training for each. Complementing this work, his research on "Gesture Recognition for Initiating Human-to-Robot Handovers" (2020, 8 citations) tackles the critical question of intent detection, framing handover initiation as a binary classification problem to prevent robots from incorrectly attempting to take objects from humans. Together, these contributions reflect Kwan's commitment to building robots that are both perceptually aware and socially responsive — qualities essential for deploying robotic assistants in everyday human environments. His research lays important groundwork for safer, more intuitive human-robot collaboration.