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About

Xiaojian Ma is a leading researcher in robotics and artificial intelligence, specializing in intuitive human-robot interaction, dexterous manipulation, and vision-based teleoperation. His most impactful contributions center on developing end-to-end deep learning frameworks that enable seamless, markerless control of robotic hands. Ma introduced TeachNet, a pioneering neural network architecture that directly maps depth images of human hands to robot joint angles, achieving visually similar poses without physical markers—a breakthrough cited over 100 times. He further advanced the field with Transteleop, a multimodal teleoperation system combining vision-based hand pose regression with IMU-based arm tracking, allowing mobile robots to be controlled intuitively via low-cost depth cameras. This work, accumulating over 70 citations, demonstrates his ability to bridge perception and action for real-world robotic applications. Ma has also explored task transfer in reinforcement learning, developing preference-based cost learning to migrate policies across tasks without expert demonstrations. His research has profound implications for assistive robotics, remote manipulation, and autonomous systems, making complex robotic control accessible and efficient. With over 180 total citations, Xiaojian Ma’s work continues to shape the future of human-robot collaboration.