Remote research methods for Human–AI–Robot Teaming

Abstract

This study focuses on methodological adaptations and considerations for remote research on Human–AI–Robot Teaming (HART) amidst the COVID‐19 pandemic. Themes and effective remote research methods were explored. Central issues in remote research were identified, such as challenges in attending to participants' experiences, coordinating experimenter teams remotely, and protecting privacy and confidentiality. Instances of experimental design overcoming these challenges were identified in methods for recruitment and onboarding, training, team task scenarios, and measurement. Three case studies are presented in which interactive in‐person testbeds for HART were rapidly redesigned to function remotely. Although COVID‐19 may have temporarily constrained experimental design, future HART studies may adopt remote research methods to expand the research toolkit.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 03, 2021
Source ID
10.1002/hfm.20929

Entities

People

  • Akuadasuo Ezenyilimba
  • Anagha Mudigonda
  • Christopher C. Corral
  • Craig J. Johnson
  • Eric Holder
  • Erin K. Chiou
  • Federico Scholcover
  • Glenn J. Lematta
  • Jimin Kim
  • Manuel Baeriswyl
  • Margaret E. Wong
  • Nancy J Cooke
  • Verica Buchanan

Organizations

  • Air Force Office of Scientific Research
  • Arizona State University
  • Defense Advanced Research Projects Agency
  • United States Army
  • United States Army Research Laboratory

Tags

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • STEM Education
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Human-Robot Interaction