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 [BibTeX] [Marc21]
Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
Type of publication: Conference paper
Citation: Girgin_IROS_2020
Publication status: Accepted
Booktitle: Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems
Year: 2020
Abstract: 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 Rényi 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.
Keywords: active learning, Bayesian Gaussian mixture model, Learning from demonstrations
Projects Idiap
Authors Girgin, Hakan
Pignat, E.
Jaquier, N.
Calinon, Sylvain
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Total mark: 0