Informing Real-Time Corrections in Corrective Shared Autonomy Through Expert Demonstrations
Michael Hagenowa, Emmanuel Senftb, R. Radwinc, Michael Gleicherb, Bilge Mutlub, M. Zinna
- aDepartment of Mechanical Engineering, University of Wisconsin–Madison, Madison, USA
- bDepartment of Computer Sciences, University of Wisconsin–Madison, Madison, USA
- cDepartment of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, USA
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Corrective Shared Autonomy is a method where human corrections are layered on top of an otherwise autonomous robot behavior. Specifically, a Corrective Shared Autonomy system leverages an external controller to allow corrections across a range of task variables (e.g., spinning speed of a tool, applied force, path) to address the specific needs of a task. However, this inherent flexibility makes the choice of what corrections to allow at any given instant difficult to determine. This choice of corrections includes determining appropriate robot state variables, scaling for these variables, and a way to allow a user to specify the corrections in an intuitive manner. This letter enables efficient Corrective Shared Autonomy by providing an automated solution based on Learning from Demonstration to both extract the nominal behavior and address these core problems. Our evaluation shows that this solution enables users to successfully complete a surface cleaning task, identifies different strategies users employed in applying corrections, and points to future improvements for our solution.
TL;DRThis letter enables efficient Corrective Shared Autonomy by providing an automated solution based on Learning from Demonstration to both extract the nominal behavior and address these core problems. (Semantic Scholar)