  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.
