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About

Yapeng Gao is a robotics researcher whose work sits at the compelling intersection of robot manipulation, computer vision, and machine learning, with a specialized focus on robotic table tennis as a demanding testbed for autonomous systems. His research addresses some of the field's most technically challenging problems: enabling robots to perceive, interpret, and respond to fast-moving, spin-varied ball trajectories in real time. Gao's landmark contribution, "A Table Tennis Robot System Using an Industrial KUKA Robot Arm" (2019, 49 citations), established a comprehensive framework for high-speed robotic play and has become a key reference in the field. His subsequent work deepened this foundation through innovative stroke learning strategies — combining policy gradient methods, model-free deep learning using GRU-based encoder-decoder architectures, and sample-efficient reinforcement learning tailored to the practical constraints of physical robotic systems. Complementing these efforts, his vision-based research introduced markerless racket pose detection and IMU-fused stroke classification, enabling robots to better understand an opponent's intentions in real time. With over 100 cumulative citations, Gao's portfolio reflects a coherent and impactful research agenda pushing the boundaries of what autonomous robots can perceive and learn in dynamic, high-speed environments.