Variational Inference with Mixture Model Approximation: Robotic Applications

Emmanuel Pignat; Teguh Lembono; Sylvain Calinon

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arXiv · 1905.09597

Variational Inference with Mixture Model Approximation: Robotic Applications

Emmanuel Pignat·Teguh Lembono·Sylvain Calinon

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Abstract

We propose a method to approximate the distribution of robot configurations satisfying multiple objectives. Our approach uses variational inference, a popular method in Bayesian computation, which has several advantages over sampling-based techniques. To be able to represent the complex and multimodal distribution of configurations, we propose to use a mixture model as approximate distribution, an approach that has gained popularity recently. In this work, we show the interesting properties of this approach and how it can be applied to a wide range of problems in robotics.

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Emmanuel Pignat, Teguh Lembono, Sylvain Calinon. 2019-05-23. Variational Inference with Mixture Model Approximation: Robotic Applications. https://arxiv.org/abs/1905.09597

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