The overarching project goal is to advance the design of opportunistic state-triggered aperiodic controllers for networked cyber-physical systems. This poster considers the problem of opportunistic human-robot collaboration to solve multi-objective optimization problems. We consider scenarios where a human decision maker works with a robot in a supervisory manner in order to find the best Pareto solution to a given optimization problem. The human has a time-invariant function that represents the value she gives to the different outcomes.
