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On human-in-the-loop optimization of human-robot interaction.
Slade, Patrick; Atkeson, Christopher; Donelan, J Maxwell; Houdijk, Han; Ingraham, Kimberly A; Kim, Myunghee; Kong, Kyoungchul; Poggensee, Katherine L; Riener, Robert; Steinert, Martin; Zhang, Juanjuan; Collins, Steven H.
Afiliación
  • Slade P; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. slade@seas.harvard.edu.
  • Atkeson C; The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Donelan JM; WearTech Labs, Simon Fraser University, Burnaby, British Columbia, Canada.
  • Houdijk H; Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Ingraham KA; Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
  • Kim M; Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USA.
  • Kong K; Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea.
  • Poggensee KL; Department of Rehabilitation Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Riener R; Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands.
  • Steinert M; Sensory-Motor Systems Lab, ETH Zurich, Zürich, Switzerland.
  • Zhang J; Faculty of Medicine, University of Zurich, Zürich, Switzerland.
  • Collins SH; Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Nature ; 633(8031): 779-788, 2024 Sep.
Article en En | MEDLINE | ID: mdl-39322732
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Robótica / Interfaz Usuario-Computador / Sistemas Hombre-Máquina Límite: Humans Idioma: En Revista: Nature Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Robótica / Interfaz Usuario-Computador / Sistemas Hombre-Máquina Límite: Humans Idioma: En Revista: Nature Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido