Abstract
Abstract 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.