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About

Zhenjie Zhao is a researcher at the intersection of robotics, human-robot interaction (HRI), and artificial intelligence, with a focus on enabling more intuitive and emotionally intelligent machines. His work addresses critical challenges in how robots perceive and respond to human behavior, particularly in dynamic, real-world settings. A major contribution is his pioneering research on sensing and handling engagement dynamics during HRI, where he developed real-time perception models to prevent communication breakdowns when humans shift their attention to peripheral devices—a problem that has garnered 30 citations. Zhao also advances robotic manipulation through bio-inspired affordance learning, introducing a transformer-based global feature encoding approach for 6-DoF grasping. In the realm of cognitive AI, he has innovated methods for learning physical common sense by framing it as a knowledge graph completion task, employing BERT data augmentation and constrained Tucker factorization to improve model generalization. His work on "Live Emoji" further explores semantic emotional expressiveness in 2D live animation, aiming to enhance emotional communication in tele-present agents. With a growing citation impact, Zhao’s research is shaping the future of socially aware and physically capable robots.