Inference of User Qualities in Shared Control of CPHS: A Contrast in Users

Lucas Hall, Urja Acharya, Justin Bradley, Brittany Duncan

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)110-117
Number of pages8
JournalIFAC-PapersOnLine
Volume51
Issue number34
DOIs
Publication statusPublished - Jan 1 2019

    Fingerprint

Keywords

  • Shared control
  • autonomous mobile robots
  • human robot interaction
  • telerobotics

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this