Learning from demonstration (LfD) is an intuitive framework allowing
non-expert users to easily (re-)program robots. However, the quality and
quantity of demonstrations have a great influence on the generalization
performances of LfD approaches. In this paper, we introduce a novel active
learning framework in order to improve the generalization capabilities of
control policies. The proposed approach is based on the epistemic uncertainties
of Bayesian Gaussian mixture models (BGMMs). We determine the new query point
location by optimizing a closed-form information-density cost based on the
quadratic R\'enyi entropy. Furthermore, to better represent uncertain regions
and to avoid local optima problem, we propose to approximate the active
learning cost with a Gaussian mixture model (GMM). We demonstrate our active
learning framework in the context of a reaching task in a cluttered environment
with an illustrative toy example and a real experiment with a Panda robot.Comment: Accepted for publication in IROS'2
