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About

Tao Han is a robotics researcher whose work spans deformable object manipulation, human-robot collaboration, and anthropomorphic robotic hand design. His most influential contribution, "3-D Deformable Object Manipulation Using Deep Neural Networks" (2019, 95 citations), introduced a deep neural network-based controller capable of servo-controlling the position and shape of deformable objects with unknown material properties — a landmark advance in addressing one of robotics' most dimensionally complex challenges. Complementing this, his 2018 work on robust real-time shape estimation tackled critical limitations in existing methods, including sensitivity to noise and occlusion, pushing deformable manipulation toward greater precision and practicality. Beyond manipulation, Han has made notable strides in human-robot interaction through an actor-critic framework for legible motion planning, enabling robots to communicate intentions more naturally to human collaborators. His engineering contributions include the CATCH-919 Hand — a 9-actuator, 19-DOF anthropomorphic robotic hand — and biomimetically-inspired finger designs grounded in anatomical analysis, both reflecting a deep commitment to bridging human biology and robotic dexterity. Collectively, his research establishes Han as a versatile contributor to intelligent, human-centered robotics systems.