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About

Ziyan Gao is a robotics researcher specializing in nonprehensile manipulation, with a particular focus on planar pushing strategies for handling novel and unknown objects. Their work addresses one of the central challenges in robotic automation: enabling robots to manipulate objects whose physical properties — such as center of mass, friction coefficients, and inertial parameters — are not known in advance. Gao's most significant contributions lie in developing intelligent frameworks for estimating object properties on the fly and leveraging that knowledge for precise manipulation. Their 2022 paper on zero moment two-edge pushing introduced a principled approach to reorienting novel objects while simultaneously estimating the center of mass, earning 11 citations and establishing them as a leading voice in this niche. Complementary work on few-shot learning and two-stage learning frameworks demonstrates a consistent drive to minimize the data requirements for robot learning in unstructured environments. With a growing body of work spanning learning-based manipulation, self-supervised state representation, and friction-aware pushing strategies, Gao has accumulated over 30 citations across their publications. Their research holds meaningful implications for industrial automation, healthcare robotics, and service robots operating in dynamic, real-world settings where adaptability to unknown objects is essential.