Cognitive Autonomy:
Human-machine interaction and teaming
Mission
The objective of this research is a new architecture for modeling, prediction, and control of cyber-physical systems with a human in the loop, that is highly responsive to the human, yet maintains safety, reliability, and high performance.
Supported by NSF CNS-1836952
Research Topics and Publication
Human behavior modeling
Shared control for human operator training
Inverse optimal control
Human subject experiments
S. Byeon, W. Jin, D. Sun and I. Hwang, "Human-Automation Interaction for Assisting Novices to Emulate Experts by Inferring Task Objective Functions," 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 2021, pp. 1-6, doi: 10.1109/DASC52595.2021.9594324. [Student Paper Awards Finalist]
S. Byeon, D. Sun and I. Hwang, "Skill-level-based Hybrid Shared Control for Human-Automation Systems*," 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 1507-1512, doi: 10.1109/SMC52423.2021.9658994.
D. Sun and I. Hwang, 2020. On Controlled Mode Discernibility for Nonlinear Hybrid Systems with Unknown Exogenous Input. Submitted to Automatica.
S. Byeon, D. Sun, and I. Hwang, "Human behavior modeling via identification of task objective and variability," Submitted to IEEE transactions on human-machine systems.
S. G. Clarke, S. Byeon and I. Hwang, "A Low Complexity Approach to Model-Free Stochastic Inverse Linear Quadratic Control," in IEEE Access, vol. 10, pp. 9298-9308, 2022, doi: 10.1109/ACCESS.2022.3144933.
People
Principal investigators
Team members
Dawei Sun
Joonwon Choi
Yutong Zhang
Sponsor and Collaboration
NSF CPS Frontier: Cognitive Autonomy for Human CPS (learn more). From 2019 to present (2022)