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. |
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Idiap |
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| Added by: | [UNK] |
| Total mark: | 0 |
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