Advancements in Iterative Learning Control towards Improved Human-Autonomy Interactions

Autonomous systems have undergone significant changes over the past five-ten years thanks to technological advancements that have been leveraged to meet a diverse set of interaction requirements driven by performance and capability needs. Conventional control strategies were typically designed for robustness and speed of the automated system within a controlled and well-regulated environment. However, recent demands for shared interactions between an automated system (including its controller), which can be modeled using first-principles techniques, and a human operator whose behavior is best understood through other modeling frameworks, have pushed the need for alternative control approaches. Making matters more challenging, the optimal blend of human input and input from the automatic control system depends sensitively on the automated system-, the environment-, and task-specific characteristics. This talk will focus on methods for utilizing cognitive hierarchy theory to characterize the bi-directional interactions between a human operator and an underlying system (vehicle + its controller), along with iterative learning techniques for deducing an optimal arbitration level between human and autonomous inputs in the context of a driver training simulator environment where a closed course is repeated. The talk will include initial driver-in-the-loop simulation results to illustrate the efficacy of the proposed approaches as well as several of the fundamental research challenges that lie ahead.