Most cyber-physical human systems (CPHS) rely on users learning how to interact with the system. Rather, a collaborative CPHS should learn from the user and adapt to them in a way that improves holistic system performance. Accomplishing this requires collaboration between the human-robot/human-computer interaction and the cyber-physical system communities in order to feed back knowledge about users into the design of the CPHS. The requisite user studies, however, are difficult, time consuming, and must be carefully designed. Furthermore, as humans are complex in their interactions with autonomy it is difficult to know, a priori, how many users must participate to attain conclusive results. In this paper we elaborate on our work to infer intrinsic user qualities through human-robot interactions correlated with robot performance in order to adapt the autonomy and improve holistic CPHS performance. We first demonstrate through a study that this idea is feasible. Next, we demonstrate that significant differences between groups of users can impact conclusions particularly where different autonomies are involved. Finally, we also provide our rich, extensive corpus of user study data to the wider community to aid researchers in designing better CPHS.
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Inference of User Qualities in Shared Control
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.
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- Award ID(s):
- 1638099
- NSF-PAR ID:
- 10092680
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 588 to 595
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/icra.2018.8461193
