On human-in-the-loop optimization of human–robot interaction

Patrick Slade (), Christopher Atkeson, J. Maxwell Donelan, Han Houdijk, Kimberly A. Ingraham, Myunghee Kim, Kyoungchul Kong, Katherine L. Poggensee, Robert Riener, Martin Steinert, Juanjuan Zhang and Steven H. Collins ()
Additional contact information
Patrick Slade: Harvard University
Christopher Atkeson: Carnegie Mellon University
J. Maxwell Donelan: Simon Fraser University
Han Houdijk: University of Groningen
Kimberly A. Ingraham: University of Washington
Myunghee Kim: University of Illinois Chicago
Kyoungchul Kong: Korea Advanced Institute of Science and Technology (KAIST)
Katherine L. Poggensee: Erasmus Medical Center
Robert Riener: ETH Zurich
Martin Steinert: Norwegian University of Science and Technology (NTNU)
Juanjuan Zhang: Nankai University
Steven H. Collins: Stanford University

Nature, 2024, vol. 633, issue 8031, 779-788

Abstract: Abstract From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human–robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human–robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-024-07697-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:633:y:2024:i:8031:d:10.1038_s41586-024-07697-2

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-024-07697-2

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().