In the last decade, several experiments were conducted to investigate human motor control behavior for the task of arm reaching, using only visual feedback of the final hand position at the end of each reaching motion. Current computational frameworks have yet to model that the humans learn to complete such a task by feedforward action based on the feedback of a displacement error at the end of past reaching motions. This paper demonstrates how such learning can be formulated as an optimization problem. By designing a cost function which weighs the tracking of the target and the smoothness of human motion, the constructed framework, implemented in the form of point-to-point learning control, inherently embeds the feedforward control and enables learning over repeated trials using only the available feedback from past observations, here the endpoint errors of a reaching motion trajectory. The proposed framework is able to reproduce the human learning behavior observed in experiments.
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