- Postdoc Fellow, Harvard University, 2017-2018
- Ph.D The University of Texas at Austin, 2016
- M.S. The University of Texas at Austin, 2013
- B.S. Harbin Institute of Technology, 2011
Dr. Ye Zhao started as an Assistant Professor at George Woodruff School of Mechanical Engineering in January 2019. Previously he was a Postdoctoral Fellow at Harvard University, where he worked on robust trajectory optimization algorithms for manipulation and locomotion problems with frictional contact behaviors. His research interests lie broadly in planning, control, and decision-making algorithms of highly agile, contact-rich, and human-cooperative robots. Dr. Zhao's previous research experience includes robust motion planning and optimization of rough terrain locomotion and dynamic manipulation, reactive task planning of whole-body locomotion interacting with uncertain and unstructured environments, distributed impedance control, whole-body operational space control of compliant and prioritized multi-task humanoid robots. He is especially interested in computationally efficient algorithms for challenging robotics problems, where performance such as robustness, autonomy, efficiency, and safety are formally guaranteed. His long-term goal is to build autonomous robots (or cyber-physical systems in general) and generalize fundamental theories and computational algorithms for intelligent and collaborative humanoid robots, mobile robots, and human assistive devices, operating in cluttered environments and working alongside humans in their daily lives.
Dr. Ye Zhao’s previous work on motion planning and control of dynamic legged locomotion collaborated with other colleagues at UT Austin, and robust trajectory optimization for dynamic manipulation tasks at Harvard.
At Georgia Tech, Dr. Zhao leads the Laboratory for Intelligent Decision and Autonomous Robots. One of the on-going lab directions is formal methods and decision-making algorithms of dynamic terrestrial locomotion and aerial manipulation in complex and human-surrounded environments. We aim at scalable planning and decision algorithms enabling heterogeneous robot teammates to dynamically interact with unstructured environments and collaborate with humans. In particular, we are interested in robust task and motion planning approaches that (i) abstract and unify diverse, complex low-level robot dynamics generally possessing under-actuated, hybrid, and nonlinear features; (ii) computationally efficient, safe and reactive decision-making algorithms explicitly taking into account dynamic environmental events and human motions. One of our primary goals is to achieve a hierarchical and scalable planning framework with the following objectives: (i) robust, non-periodic motion planners and control barrier certificates for versatile terrestrial and aerial maneuvering; (ii) game-based reactive task planner in response to diverse and possibly adversarial environmental events; (iii) novel multi-agent decision-making approaches that decompose the entire robot team into multiple sub-teams with receding horizon approaches. We will adopt algorithmic methods at the interaction of formal methods, multi-agent systems, robust control, and machine learning. The experimental performance will be evaluated on the Buzzy Cassie robot and manipulator built in the lab.
Buzzy Cassie robot in the LIDAR group for dynamic legged locomotion.
Automation and Mechatronics: robotics (legged locomotion and manipulation), robust control, real-time and reactive motion planning, applied optimization algorithm, autonomy, and formal method based decision-making.