EMG-Based Shared Control Framework for Human-Robot Co-Manipulation Tasks

The paper presents a shared control architecture designed for human-robot co-manipulation tasks, that allows the human to switch among robot”s operational modes through surface electromyography (sEMG) signals from the user”s arm. A support vector machine (SVM) classifier is employed to process the raw EMG data to identify two classes of contractions that are fed into a finite state machine algorithm to trigger the activation of different sets of admittance control parameters corresponding to the envisaged operational modes. The proposed architecture has been experimentally validated using a Kinova Jaco2 manipulator, equipped with Force/Torque sensor at the end-effector, and with a user wearing Delsys Trigno Avanti EMG sensors on the dominant upper limb.