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Functional electrical stimulation is a promising technique for restoring arm function to those with paralysis from a high spinal cord injury. While simple controllers are easy to implement, model-based controllers are likely better equipped to leverage the arm’s kinematic and dynamic complexity, particularly for the high variations associated with functional arm movement. One modelling technique for a model-based controller is Gaussian Process Regression. Previous simulation work has shown promise leveraging whole-arm error data to identify the arm’s various subsystems, but used perfect simulated data. We asked caregivers to correct a robotic arm’s movement as simulated muscles generated torque. The simulated muscles were controlled as if they were electrically stimulated human arm muscles. This study demonstrates non-expert caregivers’ ability to collect this error data via whole-arm corrections, and provides insight into their ability to improve arm subsystem models made with Gaussian Process Regression. Despite significant error in caregivers’ ability to provide force corrections to hold the robot in a static configuration, these corrections were leveraged to significantly improve muscle models; the muscles that improved the most were the ones primarily used to move the physiologically actuated robot.