Inference of User Qualities in Shared Control. Acharya, U., Kunde, S., Hall, L., Duncan, B., & Bradley, J. In 2018 IEEE International Conference on Robotics and Automation, pages 588–595, Brisbane, Australia, May, 2018. 2018 IEEE International Conference on Robotics and Automation. doi abstract bibtex Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.
@inproceedings{acharya2018inference,
address = {Brisbane, Australia},
title = {Inference of {User} {Qualities} in {Shared} {Control}},
doi = {10.1109/ICRA.2018.8461193},
abstract = {Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.},
booktitle = {2018 {IEEE} {International} {Conference} on {Robotics} and {Automation}},
publisher = {2018 IEEE International Conference on Robotics and Automation},
author = {Acharya, Urja and Kunde, Siya and Hall, Lucas and Duncan, Brittany and Bradley, Justin},
month = may,
year = {2018},
keywords = {Collision avoidance, Force, Human-Robot Interaction, Human-robot interaction, NSF 1638099, Robots, System performance, Task analysis, Telepresence, collaborative performance, groupware, human-robot interaction, locus of control, robotic systems, shared control, teleoperation controllers, telepresence robot, telerobotics},
pages = {588--595},
}
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