Hao Zhang's research focuses on lifelong collaborative autonomy, with a particular emphasis on robot adaptation, multi-robot collaboration, and human-robot teaming. The goal of his work is to enable robots to operate and adapt over extended periods while seamlessly collaborating with humans and other robots as teammates.

He develops innovative algorithms grounded in foundation models, machine learning control, graph theory, and bilevel optimization with provable properties to facilitate lifelong collaborative autonomy. Additionally, Zhang is interested in interdisciplinary research aimed at creating fundamentally new software architectures and integrated autonomy stacks that can be demonstrated and deployed on real robots. These algorithms and systems are envisioned as promising solutions and critical components for a variety of real-world robotics applications, including manufacturing, homeland defense, connected autonomous driving, environmental monitoring, and the Internet of Robotic Things.

Zhang is a recipient of the NSF CAREER Award, the DARPA Young Faculty Award (YFA), and the DARPA Director's Fellowship. His work has won several best paper awards and nominations at premier robotics conferences, including RSS, ICRA, and IROS. He regularly serves on the technical program committees and editorial boards of top-tier conferences and journals in the fields of robotics, machine learning, and artificial intelligence. One of his passions is promoting robotics among young and underrepresented aspiring roboticists through his outreach program, PROGRESS (Program for Robotics Outreach on Gender and Racial Equity in School and Society).