Learning from demonstration with model-based Gaussian process

11 Oct 2019Noémie JaquierDavid GinsbourgerSylvain Calinon

In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task. In this paper, we propose a novel multi-output Gaussian process (MOGP) based on Gaussian mixture regression (GMR)... (read more)

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