Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces

Jongmin M. Lee*, Temesgen Gebrekristos, Dalia D.E. Santis, Mahdieh Nejati-Javaremi, Deepak Gopinath, Biraj Parikh, Ferdinando Mussa Ivaldi, Brenna D. Argall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a sensor net of four inertial measurement units, placed bilaterally on the upper body, and a BoMI with the capacity to directly control a robot in six dimensions. We consider whether the way in which the robot control space is mapped from human inputs has any impact on learning. Our results suggest that the space of robot control does play a role in the evolution of human learning: specifically, though robot control in joint space appears to be more intuitive initially, control in task space is found to have a greater capacity for longer-term improvement and learning. Our results further suggest that there is an inverse relationship between control dimension couplings and task performance.

Original languageEnglish (US)
Article number38
JournalACM Transactions on Human-Robot Interaction
Volume13
Issue number3
DOIs
StatePublished - Aug 26 2024

Funding

Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under Award Number R01-EB024058; the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) of the National Institutes of Health (NIH), Award Number T32-HD007418; the National Science Foundation (NSF), Award Number 2054406; National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR), Award Number 90REGE0005-01-00; and European Union\u2019s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie, Project REBoT, Award Number GA-750464. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Keywords

  • assistive manipulator
  • Body-machine interface
  • motor learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Artificial Intelligence

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