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.
翻译:我们建议一种方法来估计满足多种目标的机器人配置的分布。 我们的方法使用不同推论,一种贝叶斯计算中流行的方法,比基于取样的技术有几种优势。为了能够代表复杂和多式的配置分布,我们建议使用混合模型作为近似分布,这种方法最近越来越受欢迎。在这项工作中,我们展示了这一方法的有趣特性,以及如何将其应用于机器人的多种问题。