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 [BibTeX] [Marc21]
Variational Inference with Mixture Model Approximation for Applications in Robotics
Type of publication: Conference paper
Citation: Pignat_ICRA_2020
Publication status: Accepted
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.
Projects Idiap
Authors Pignat, Emmanuel
Lembono, Teguh Santoso
Calinon, Sylvain
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Total mark: 0
  • Pignat_ICRA_2020.pdf