Section: New Results

Applications for Robotic myoelectric prostheses: co-adaptation algorithms and design of a 3D printed robotic arm prosthesis

Participants : Pierre-Yves Oudeyer [correspondant] , Manuel Lopes, Joel Ortiz, Mathilde Couraud, Aymar de Rugy, Daniel Cattaert, Florent Paclet.

Together with the Hybrid team at INCIA, CNRS, the Flowers team continued to work on establishing the foundations of a long-term project related to the design and study of myoelectric robotic prosthesis. The ultimate goal of this project is to enable an amputee to produce natural movements with a robotic prosthetic arm (open-source, cheap, easily reconfigurable, and that can learn the particularities/preferences of each user). This will be achieved by 1) using the natural mapping between neural (muscle) activity and limb movements in healthy users, 2) developing a low-cost, modular robotic prosthetic arm and 3) enabling the user and the prosthesis to co-adapt to each other, using machine learning and error signals from the brain, with incremental learning algorithms inspired from the field of developmental and human-robot interaction. In particular, in 2015 two lines of work were achieved, concerning two important scientific challenges, and in the context of a PEPS CNRS project:

First, an experimental setup was designed to allow fast prototyping of 3D printed robotic prostheses (internship of Joel Ortiz). This work was based on the use of the Poppy open-source modular platform, and resulted in a functional prototype. Several video demonstrations are available at: https://forum.poppy-project.org/t/real-time-control-of-a-prosthetic-robotic-arm-poppy-with-muscle-activities/1656 .

Second, first versions of co-adaptation algorithms were designed, implemented and tested with human subjects, based on the combination of advanced models of the arm biomechanics and incremental learning algorithms (internship of Mathilde Couraud). An article is under preparation.