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Reaching optimally over the workspace: A machine learning approach

Recent theories of Human Motor Control explain our outstanding coordination capabilities by calling upon an Optimal Control (OC) framework. But OC methods are generally too expensive to be applied on-line and in realtime as would be required to perform everyday movements. An alternative method consists in obtaining a pre-computed feedback policy that performs optimally while being executed reactively. One way to get such a pre-computed policy consists in tuning a parametrized reactive controller so that it converges to optimal behavior. In this paper, we demonstrate a method to obtain such a reactive controller that (i) adapts the time of movement based on a compromise between the amount of reward and the effort required to get it, (ii) provides an efficient trajectory from any point to any point in the workspace, (iii) learns from demonstrations of optimal trajectories, (iv) is improving its performance over accumulated experience.