Human motor performance is a key area of investigation in both biomechanics and humanoid robotics. In robotics, understanding human neuromuscular control is important to synthesize prosthetic motions and ensure safe human-robot interaction. Building controllable biomechanical models through modeling and algorithmic tools from both robotics and biomechanics increases our scientific understanding of neuromusculoskeletal mechanics and control. The resulting models can consequently help quantifying the characteristics of a subject’s motion and in designing effective treatments, like motion training. The objective of this paper is to explore how neural control dictates motor performance in humans by developing a computational framework that enables robotics-based control and simulation of the human musculoskeletal system. More specifically: (1) computational models of the human musculoskeletal system for robotics-based control were developed; (2) performance metrics were integrated for motion characterization based on a subject’s physiological constraints; and (3) robust control and simulation algorithms were integrated to synthesize movement using biomechanical models that accurately match experimental data. Motion capture experiments were conducted to tune subject-specific parameters. To investigate the effects of robotic assistance as a means of increasing the efficiency in motor movements an experiment was designed in which subjects will initially performed a basic task and then performed the same task with the assistance of the six degrees-of-freedom JACO robotic arm. Initial results showed that robotic assistance was efficient in decreasing the muscle activation for major arm muscles.

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