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While significant advancements have been made in the mechanical andtask-specific controller designs of powered transfemoral prostheses, developinga task-adaptive control framework that generalizes across various locomotionmodes and terrain conditions remains an open problem. This study proposes atask-adaptive learning quasi-stiffness control framework for powered prosthesesthat generalizes across tasks, including the torque-angle relationshipreconstruction part and the quasi-stiffness controller design part.Quasi-stiffness is defined as the slope of the human joint's torque-anglerelationship. To accurately obtain the torque-angle relationship in a new task,a Gaussian Process Regression (GPR) model is introduced to predict the targetfeatures of the human joint's angle and torque in the task. Then a KernelizedMovement Primitives (KMP) is employed to reconstruct the torque-anglerelationship of a new task from multiple human demonstrations and estimatedtarget features. Based on the torque-angle relationship of the new task, aquasi-stiffness control approach is designed for a powered prosthesis. Finally,the proposed framework is validated through practical examples, includingvarying speed and incline walking tasks. The proposed framework has thepotential to expand to variable walking tasks in daily life for thetransfemoral amputees.

Learning Task-adaptive Quasi-stiffness Control for A Powered Transfemoral Prosthesis