Learning efficient control of robots using myoelectric interfaces

Myoelectric controlled interfaces are a vital component for advancing applications in prostheses, exoskeletons, and robot teleoperation. Current methods search for optimal neural decoders for enhanced initial user performance. However, recent studies demonstrate learning an inverse model of abstract decoders to improve performance over time. This paper proposes a paradigm shift on myoelectric interfaces by embedding the human as controller of a...