An adjustable autonomy paradigm for adapting to expert-novice differences

Multi-robot manipulation tasks are challenging for robots to complete in an entirely autonomous way due to the perceptual and cognitive requirements of grasp planning, necessitating the development of specialized user interfaces. Yet even for humans, the task is sufficiently complex that a high level of performance variability exists between a novice and an expert's ability to teleoperate the robots in a sufficiently tightly coupled fashion to manipulate objects without dropping them. The ultimate success of the task relies on the skill level of the human operator to manage and coordinate the robot team. Although most systems focus their effort on forging a unified connection between the robots and the operator, less attention has been spent on the problem of identifying and adapting to the human operator's skill level. In this paper, we present a method for modeling the human operator and adjusting the autonomy levels of the robots based on the operator's skill level. This added functionality serves as a crucial mechanism toward making human operators of any skill level a vital asset to the team even when their teleoperation performance is uneven.