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About

Boling Yang is a robotics researcher whose work spans robot manipulation, human-robot interaction, and multi-agent learning. With a focus on advancing robotic dexterity and perception, Yang has made notable contributions to how robots sense, grasp, and interact with objects across vastly different scales. His early work on pre-touch sensing — now among his most cited, with 39 citations — demonstrated how optical time-of-flight proximity sensors can significantly improve sequential manipulation tasks, while follow-up research showed these sensors could also enhance object pose estimation in complex kinematic chains. Yang further expanded the manipulation frontier by investigating ultrasonic levitation as a contactless strategy for handling millimeter-scale objects, a domain where conventional robotic grippers fall short. His proposal of the Rubik's Cube as a standardized manipulation benchmark (20 citations) reflects a commitment to rigorous, reproducible evaluation across robot platforms. Beyond manipulation, Yang has explored competitive human-robot interaction — developing fencing and exercise games to study embodied AI in adversarial settings — and applied Stackelberg game theory to autocurricular multi-agent reinforcement learning. Together, his body of work positions him as a versatile contributor bridging physical robotics, sensing, and intelligent behavior.