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Opportunistic robot control for interactive multiobjective optimization under human performance limitations
P. Ong, J. Cortés
Automatica 123 (2021), 109263

Abstract

This paper proposes opportunistic state-triggered strategies for solving convex multiobjective optimization problems that involve human-robot interaction. The robot is aware of the multiple objective functions defining the problem, but requires human input to find the most desirable Pareto solution. In order to avoid overloading the human with queries, we view her as a limited resource to the robot, and design event-triggered controllers that opportunistically prescribe the information exchanges among them. We consider various models of human performance, starting with an ideal one where queries are responded instantaneously, and later considering constraints on the time taken to respond and the minimum time between consecutive queries. For each model, we formally establish the asymptotic convergence to the desired optimizer and rule out the existence of Zeno behavior.

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