This Faculty Early Career Development (CAREER) Program grant addresses control and decision-making for human-robot collaborative teams. The project has two main research thrusts. The first thrust examines a team consisting of a skilled human with knowledge of a certain manufacturing or sensing task, and a partially trained robot assistant that is capable of endless repetition without boredom or fatigue. The workload balance between the human and robot is governed by the trust that the human has for the robot, which is modeled mathematically as a function of the rate of improvement in performance and the rate of decrease in number of mistakes. Innovative trust-based algorithms will provide a balanced human experience and guaranteed team performance. The second research thrust will create novel planning strategies incorporating mathematical models of regret, an emotion central to human rational decision-making. Regret-based automatic decision-making aids will provide more human-like decisions for more natural human-robot interaction. Results from both thrusts of the project will ultimately enable transformative human-robot interaction technologies benefitting the U.S. economy and quality of life. The educational initiatives of this project will broaden participation of underrepresented groups in manufacturing and robotics research.
The technical approach entails the integration of trust into cooperative control and regret into decision-making for human-agent collaborative teams. To explore new fundamental understanding of trust and realize effective control allocation, the first thrust will develop new dynamic trust models based on qualitative results from human factors research, and novel trust-based control strategies for human-agent collaborative teams modeled by switched systems. Non-conservative multiple Lyapunov functions based analysis will be developed to provide state-dependent switching control of the manual and autonomous modes. The second thrust features the development of a regret-based Bayesian sequential decision-making framework that selects between the manual and autonomous mode in a way such that suboptimal decisions will be made to avoid the possible regret of making a wrong decision. Both thrusts include experimental validations using a heterogeneous multi-robot test bed and a humanoid manufacturing robot with human-in-the-loop. Results from this research will foster a new interface between control theory and human factors.
