Standard Deep Generative Models for Density Estimation in Configuration Spaces: A Study of Benefits, Limits and Challenges
Robert Gieselmann,Florian T. Pokorny,Robert Gieselmann,Florian T. Pokorny
Deep Generative Models such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) have found multiple applications in Robotics, with recent works suggesting the potential use of these methods as a generic solution for the estimation of sampling distributions for motion planning in parameterized sets of environments. In this work we provide a first empirical study of challenge...
