Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies

An important challenge when using reinforcement learning for learning motions in robotics is the choice of parameterization for the policy. We use Gaussian Mixture Regression to extract a parameterization with relevant non-linear features from a set of demonstrations of a motion following the paradi...

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