CONF Pignat_ICRA_2020/IDIAP Variational Inference with Mixture Model Approximation for Applications in Robotics Pignat, Emmanuel Lembono, Teguh Santoso Calinon, Sylvain EXTERNAL http://publications.idiap.ch/attachments/papers/2020/Pignat_ICRA_2020.pdf PUBLIC International Conference on Robotics and Automation 2020 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.