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@INPROCEEDINGS{Girgin_IROS_2020,
         author = {Girgin, Hakan and Pignat, E. and Jaquier, N. and Calinon, Sylvain},
       keywords = {active learning, Bayesian Gaussian mixture model, Learning from demonstrations},
       projects = {Idiap},
          title = {Active Improvement of Control Policies with Bayesian Gaussian Mixture Model},
      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 R{\'{e}}nyi 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.}
}