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Bayesian Inference for Efficient Learning in Control

contrast to humans or animals, artiﬁcial learners often require more trials when learning motor control tasks solely based on experience. Eﬃcient autonomous learners will reduce the amount of engineering required to solve control problems. By using probabilistic forward models, we can employ two key ingredients of biological learning systems to speed up artiﬁcial learning. We present a consistent and coherent Bayesian framework that allows for eﬃcient autonomous experience-based learning. We demonstrate the success of our learning algorithm by applying it to challenging nonlinear control problems in simulation and in hardware.