Contingency-Aware Task Assignment and Scheduling for Human-Robot Teams

Abstract

We consider the problem of task assignment and scheduling for human-robot teams to enable the efficient completion of complex problems, such as satellite assembly. In high-mix, low volume settings, we must enable the human-robot team to handle uncertainty due to changing task requirements, potential failures, and delays to maintain task completion efficiency. We make two contributions: (1) we account for the complex interaction of uncertainty that stems from the tasks and the agents using a multi-agent concurrent MDP framework, and (2) we use Mixed Integer Linear Programs and contingency sampling to approximate action values for task assignment. Our results show that our online algorithm is computationally efficient while making optimal task assignments compared to a value iteration baseline. We evaluate our method on a 24-task representative assembly and a real-world 60-task satellite assembly, and we show that we can find an assignment that results in a near-optimal makespan.