Online Scheduling and Reactive Behaviors for Effective Human-Robot-Collaboration

Anonymous

Keywords: Human-Robot Collaboration, Constraint Programming, Behavior Trees, Uncertainty-aware scheduling

TL;DR: The paper describes an uncertainty-aware scheduling approach that is embeded in a reactive execution engine based on behavior trees.

Abstract: Assembly processes involving humans and robots are challenging scenarios because the individual activities and access to shared workspace have to be coordinated. Fixed robot programs leave no room to diverge from a fixed protocol. Working on such a process can be very stressful for the user and lead to ineffective behavior or failure. To this end, we propose a novel approach of utilizing uncertainty-aware constraint-based scheduling in a reactive execution control framework facilitating behavior trees. This allows the robot to adapt to uncertain events such as delayed activity completions and activity selection (by the human). The user can rest assured that the robot will wait for him to finish his activity and will adapt to best complement the human-selected activities to complete the common task. In addition to the improved working conditions, our algorithm also leads to increased efficiency, even in scenarios with increased uncertainty. We evaluate our algorithm using a probabilistic simulation study and initial real robot experiments using a Franka Emika Panda robot and human tracking based on HTC Vive VR gloves.