Tao Ye

Technical University of Munich, Zhejiang University

Papers

Total Citations

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About

Tao Ye is a researcher whose work bridges robotics, machine learning, and low-cost fabrication. In robotics, Ye developed a semi-supervised online learning algorithm for classifying objects in 3D data streams, significantly reducing the need for human supervision in mobile robotics—a key step toward greater autonomy. This work has garnered 4 citations and addresses the challenge of processing large-scale, real-time data. More recently, Ye has pioneered accessible actuation technology with the "Printed Paper Actuator," a low-cost, reversible electrical actuation and sensing method created by printing conductive PLA on copy paper using a standard desktop 3D printer. This innovation, earning 2 citations, opens doors for rapid prototyping and educational applications by making smart materials widely available. Ye’s contributions stand out for their dual focus: advancing machine learning efficiency in robotics while democratizing fabrication through simple, scalable techniques. This combination of computational and physical innovation marks Ye as a versatile thinker, pushing boundaries in both autonomous systems and interactive materials.