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In this paper, we consider the problem of allocating human operators in asystem with multiple semi-autonomous robots. Each robot is required to performan independent sequence of tasks, subjected to a chance of failing and gettingstuck in a fault state at every task. If and when required, a human operatorcan assist or teleoperate a robot. Conventional MDP techniques used to solvesuch problems face scalability issues due to exponential growth of state andaction spaces with the number of robots and operators. In this paper we deriveconditions under which the operator allocation problem is indexable, enablingthe use of the Whittle index heuristic. The conditions can be easily checked toverify indexability, and we show that they hold for a wide range of problems ofinterest. Our key insight is to leverage the structure of the value function ofindividual robots, resulting in conditions that can be verified separately foreach state of each robot. We apply these conditions to two types of transitionscommonly seen in remote robot supervision systems. Through numericalsimulations, we demonstrate the efficacy of Whittle index policy as anear-optimal and scalable approach that outperforms existing scalable methods.

Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach