The goal of this project is the advancement of machine learning dynamic models and real-time control systems for human cyber-physical balance systems. Ranging from biped walkers and human bicycle riding to human-controlled helicopters, human cyber-physical balance systems maintain challenging tasks of simultaneously trajectory-tracking and unstable platforms balancing. Although many physical models were developed in past decades, it is still challenging to safely and effectively operate these human-in-the-loop balance machines in highly variable, uncertain environments. This project will develop machine learning-based mathematical models and robust control strategies for human cyber-physical balance systems. The researchers will also develop a number of integrated research and education programs to attract students from underrepresented groups into engineering and involve undergraduate students into research. Human cyber-physical balance systems involve human movements as physical and forceful interactions with unstable, underactuated platforms. It is challenging to capture and control physical human-machine or human-robot interactions in complex, uncertain environments. This project will focus on: (1) development of machine learning-based models and characterization for human cyber-physical balance systems; (2) development of new hardware/software co-design accelerated learning-based real-time control to handle human cyber-physical balance system dynamics in highly variable, uncertain environments; and (3) robotic testbeds development, experimental validation and performance evaluation. The integration of data-driven model and learning-based control strategies, along with the hardware/software co-design enabled real-time implementation, provides new perspectives on performance enhancement of safety-critical or mission-critical cyber-physical systems in dynamic, uncertain environments.