Dynamic Task Allocation for Human-Robot Teams

Abstract

Artificial agents, such as robots, are increasingly deployed for teamwork in dynamic, high-demand environments. This paper presents a framework, which applies context information to establish task (re)allocations that improve human-robot team’s performance. Based on the framework, a model for adaptive automation was designed that takes the cognitive task load (CTL) of a human team member and the coordination costs of switching to a new task allocation into account. Based on these two context factors, it tries to optimize the level of autonomy of a robot for each task. The model was instantiated for a single human agent cooperating with a single robot in the urban search and rescue domain. A first experiment provided encouraging results: the cognitive task load of participants mostly reacted to the model as intended. Recommendations for improving the model are provided, such as adding more context information.