Informing Real-time Corrections in Corrective Shared Autonomy Through Expert Demonstrations
Autor: | Michael R. Zinn, Michael Hagenow, Bilge Mutlu, Emmanuel Senft, Michael Gleicher, Robert G. Radwin |
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Rok vydání: | 2021 |
Předmět: |
Flexibility (engineering)
FOS: Computer and information sciences Robot kinematics State variable Control and Optimization Computer science Mechanical Engineering Biomedical Engineering Autonomous robot Computer Science Applications Task (project management) Human-Computer Interaction Computer Science - Robotics Artificial Intelligence Control and Systems Engineering Human–computer interaction Control theory Task analysis Robot Computer Vision and Pattern Recognition Robotics (cs.RO) |
DOI: | 10.48550/arxiv.2107.04836 |
Popis: | 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 paper 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. Comment: IEEE Robotics and Automation Letters (RA-L) |
Databáze: | OpenAIRE |
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