Variational Inference with Mixture Model Approximation: Robotic Applications
Robotics
2019-11-25 v3
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.
Cite
@article{arxiv.1905.09597,
title = {Variational Inference with Mixture Model Approximation: Robotic Applications},
author = {Emmanuel Pignat and Teguh Lembono and Sylvain Calinon},
journal= {arXiv preprint arXiv:1905.09597},
year = {2019}
}