Variational Inference with Mixture Model Approximation: Robotic Applications

TL;DR

This paper introduces a variational inference method using mixture models to approximate complex, multimodal robot configuration distributions, enhancing multi-objective robotic planning.

Contribution

It presents a novel application of mixture model-based variational inference for robot configuration distribution approximation, improving over sampling methods.

Findings

Effective approximation of multimodal distributions

Applicable to diverse robotic problems

Advantages over sampling-based techniques

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.