Kamenar Research Group

Loss of hand function due to conditions like stroke or multiple sclerosis impacts daily activities. Robotic rehabilitation provides tools to restore hand function, while surface electromyography (sEMG) enables the adaptation of the device’s force output to the user’s condition, thus enhancing rehabilitation outcomes. This study focuses on accurately predicting grip force during medium wrap grasps using a single sEMG sensor pair, addressing the challenge of escalating sensor requirements. We conducted sEMG measurements on 13 subjects at two forearm positions, validating results with a hand dynamometer. Established flexible signal-processing steps achieved high peak cross-correlations between the processed sEMG signal and grip force. Influential parameters were subsequently identified through sensitivity analysis. Leveraging a novel data-driven Koopman-based approach and problem-specific data lifting, we devised a method for the estimation and short-term prediction of grip force from processed sEMG signals. The method achieved a weighted mean absolute percentage error (wMAPE) of  5.5% for grip force estimation and  17.9% for 0.5-second predictions. The methodology proved robust regarding precise electrode positioning, as the effect of sensing position on error metrics was non-significant. The algorithm executes exceptionally fast, processing, estimating, and predicting a 0.5-second sEMG signal batch in just  30 ms, facilitating real-time implementation.

Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.