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About

Changsu Ha is a robotics researcher whose work spans teleoperation systems, mobile manipulation, flexible structure control, and robot learning. His most influential contributions center on designing intuitive and semi-autonomous frameworks for controlling complex robotic systems remotely. His 2015 paper on whole-body multi-modal teleoperation of mobile manipulators, which has garnered 25 citations, introduced a motion capture-based approach that frees operators from the constraints of traditional master interfaces — a significant step toward natural human-robot interaction. Building on this, his 2018 work on platoon teleoperation demonstrated sophisticated multi-robot coordination using peer-to-peer communication and SLAM, accumulating 16 citations. Ha has also made notable contributions to passivity-based control of manipulator-stage systems mounted on flexible beams, addressing the challenging coupled problem of end-effector tracking and vibration suppression with both theoretical and experimental validation. More recently, his research has expanded into data-driven robot learning, including an action chunking transformer for imitation learning and an RGBD-based grasping network tailored for household tableware manipulation. Across these diverse domains, Ha's research reflects a sustained commitment to bridging theoretical rigor with practical robotic deployments in complex, real-world settings.