Frontiers in Neurorobotics (Jun 2022)

Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding

  • David A. Handelman,
  • Luke E. Osborn,
  • Tessy M. Thomas,
  • Andrew R. Badger,
  • Margaret Thompson,
  • Robert W. Nickl,
  • Manuel A. Anaya,
  • Jared M. Wormley,
  • Gabriela L. Cantarero,
  • David McMullen,
  • Nathan E. Crone,
  • Brock Wester,
  • Pablo A. Celnik,
  • Matthew S. Fifer,
  • Francesco V. Tenore

DOI
https://doi.org/10.3389/fnbot.2022.918001
Journal volume & issue
Vol. 16

Abstract

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Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.

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