Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation
Name
delpreto-lipton_helping-robots-learn_icra2020.pdf
Description
Accepted version
Size
1.91 MB
Format
Adobe PDF
Checksum (MD5)
65a5b556209a5cf3913dbc3ca86f8c3e
Author(s)
DelPreto, Joseph
Lipton, Jeffrey I.
Sanneman, Lindsay
Fay, Aidan J.
Fourie, Christopher
Choi, Changhyun
Rus, Daniela
Date Issued
May 2020
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
IEEE
Citation
DelPreto, Joseph, Lipton, Jeffrey I., Sanneman, Lindsay, Fay, Aidan J., Fourie, Christopher et al. 2020. "Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation." Proceedings - IEEE International Conference on Robotics and Automation.
Version
Author's final manuscript
Abstract
© 2020 IEEE. As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra40945.2020.9196754
