Variational Inference with Mixture Model Approximation for Applications in Robotics
Emmanuel Pignat,Teguh Lembono,Sylvain Calinon,Emmanuel Pignat,Teguh Lembono,Sylvain Calinon
We propose to formulate the problem of representing a distribution of robot configurations (e.g. joint angles) as that of approximating a product of experts. Our approach uses variational inference, a popular method in Bayesian computation, which has several practical advantages over sampling-based techniques. To be able to represent complex and multimodal distributions of configurations, mixture ...
