  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). The proposed approach encapsulates the
variability retrieved from the demonstrations in the covariance of the MOGP.
Leveraging the generative nature of GP models, our approach can efficiently
modulate trajectories towards new start-, via- or end-points defined by the
task. Our framework allows the robot to precisely track via-points while being
compliant in regions of high variability. We illustrate the proposed approach
in simulated examples and validate it in a real-robot experiment.
