Task Selection and Planning in Human-Robot Collaborative Processes: To be a Leader or a Follower?

Abstract

Recent advances in collaborative robots have provided an opportunity for the close collaboration of humans and robots in a shared workspace. To exploit this collaboration, robots need to plan for optimal team performance while considering human presence and preference. This paper studies the problem of task selection and planning in a collaborative, simulated scenario. In contrast to existing approaches, which mainly involve assigning tasks to agents by a task allocation unit and informing them through a communication interface, we give the human and robot the agency to be the leader or follower. This allows them to select their own tasks or even assign tasks to each other. We propose a task selection and planning algorithm that enables the robot to consider the human’s preference to lead, as well as the team and the human’s performance, and adapts itself accordingly by taking or giving the lead. The effectiveness of this algorithm has been validated through a simulation study with different combinations of human accuracy levels and preferences for leading.