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@INPROCEEDINGS{Pignat_ICRA_2020,
         author = {Pignat, Emmanuel and Lembono, Teguh Santoso and Calinon, Sylvain},
       projects = {Idiap},
          title = {Variational Inference with Mixture Model Approximation for Applications in Robotics},
      booktitle = {International Conference on Robotics and Automation},
           year = {2020},
       abstract = {We propose to formulate the problem of repre-
senting a distribution of robot configurations (e.g. joint angles)
as that of approximating a product of experts. Our approach
uses variational inference, a popular method in Bayesian
computation, which has several practical advantages over
sampling-based techniques. To be able to represent complex and
multimodal distributions of configurations, mixture models are
used as approximate distribution. We show that the problem
of approximating a distribution of robot configurations while
satisfying multiple objectives arises in a wide range of problems
in robotics, for which the properties of the proposed approach
have relevant consequences. Several applications are discussed,
including learning objectives from demonstration, planning,
and warm-starting inverse kinematics problems. Simulated
experiments are presented with a 7-DoF Panda arm and a
28-DoF Talos humanoid.},
            pdf = {https://publications.idiap.ch/attachments/papers/2020/Pignat_ICRA_2020.pdf}
}