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.
|Original language||English (US)|
|Title of host publication||2020 IEEE International Conference on Robotics and Automation, ICRA 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - May 2020|
|Event||2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France|
Duration: May 31 2020 → Aug 31 2020
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2020 IEEE International Conference on Robotics and Automation, ICRA 2020|
|Period||5/31/20 → 8/31/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS This work was funded in part by the Boeing Company. Part of this work was conducted during MIT’s “Human Systems Engineering” course taught by Professor Leia Stirling.
© 2020 IEEE.