Variational Inference with Mixture Model Approximation: Robotic Applications
This work addresses the challenge of handling complex, multimodal distributions in robotics, but it appears incremental as it builds on existing variational inference and mixture model techniques.
The authors tackled the problem of approximating the distribution of robot configurations that meet multiple objectives by using variational inference with a mixture model approximation, showing its applicability across various robotics problems.
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.