Combining Unsupervised Muscle Co-Contraction Estimation with Bio-Feedback Allows Augmented Kinesthetic Teaching

Nowadays, an increasingly diversification of products and production lines would largely benefit from intuitive and multimodal robot teaching strategies. The present article proposes an augmented kinesthetic teaching system, which is based on surface electromyographic (sEMG) measurements from the operator forearm. Specifically, sEMG signals are used for minimal-training unsupervised estimation of forearm's muscles co-contraction level. In this way, also exploiting a vibrotactile bio-feedback, we evaluate the ability of operators in stiffening their hand-during kinesthetic teaching-in order to modulate the estimated level of muscle co-contraction to (i) match target levels and (ii) command the opening/closing of a gripper, i.e. in exploiting their sEMG signals for effective augmented robot kinesthetic teaching tasks. Experiments were carried out involving ten subjects in two different kind of experimental sessions, in order to test both co-contraction modulation abilities, and actual usage of the co-contraction for programming robot functionalities during kinesthetic teaching. The obtained results provide positive outcomes on the intuitiveness and effectiveness of the proposed system and approach, paving the way to a new generation of advanced teaching by demonstration interfaces.