@article{1efe3496ae7e4dbdaba76009958e10e7,
title = "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.",
keywords = "COVID-19, Human–AI–Robot Teaming, remote research, research methods",
author = "Lematta, {Glenn J.} and Corral, {Christopher C.} and Verica Buchanan and Johnson, {Craig J.} and Anagha Mudigonda and Federico Scholcover and Wong, {Margaret E.} and Akuadasuo Ezenyilimba and Manuel Baeriswyl and Jimin Kim and Eric Holder and Chiou, {Erin K.}",
note = "Funding Information: We thank the various funding sources that supported material in this study: The material for Case Study I is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001119C0130 administered by the U.S. Army Research Office; Material for Case Study II is supported by Air Force Office of Scientific Research (AFOSR) FA9550‐18‐1‐0067; Lastly, material for Case Study III is supported by the U.S. Army Research Laboratory, under the Cooperative Agreement No. W911‐NF‐18‐2‐0271. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Project Agency, the Air Force Office of Scientific Research, or the Army Research Laboratory or the U.S. government. We also thank Jared Freeman, Matthew Wood, Adam Fouse, and Samantha Dubrow who provided inputs for this manuscript. Funding Information: We thank the various funding sources that supported material in this study: The material for Case Study I is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001119C0130 administered by the U.S. Army Research Office; Material for Case Study II is supported by Air Force Office of Scientific Research (AFOSR) FA9550-18-1-0067; Lastly, material for Case Study III is supported by the U.S. Army Research Laboratory, under the Cooperative Agreement No. W911-NF-18-2-0271. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Project Agency, the Air Force Office of Scientific Research, or the Army Research Laboratory or the U.S. government. We also thank Jared Freeman, Matthew Wood, Adam Fouse, and Samantha Dubrow who provided inputs for this manuscript. Publisher Copyright: {\textcopyright} 2021 Wiley Periodicals LLC",
year = "2022",
month = jan,
doi = "10.1002/hfm.20929",
language = "English (US)",
volume = "32",
pages = "133--150",
journal = "Human Factors and Ergonomics In Manufacturing",
issn = "1090-8471",
publisher = "John Wiley and Sons Inc.",
number = "1",
}