Standard Deep Generative Models for Density Estimation in Configuration Spaces: A Study of Benefits, Limits and Challenges

Abstract

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 challenges, benefits and drawbacks of utilizing vanilla GANs and VAEs for the approximation of probability distributions arising from sampling-based motion planner path solutions. We present an evaluation on a sequence of simulated 2D configuration spaces of increasing complexity and a 4D planar robot arm scenario and find that vanilla GANs and VAEs both outperform classical statistical estimation by an n-dimensional histogram in our chosen scenarios. We furthermore highlight differences in convergence and noisiness between the trained models and propose and study a benchmark sequence of planar C-space environments parameterized by opened or closed doors. In this setting, we find that the chosen geometrical embedding of the parameters of the family of considered C-spaces is a key performance contributor that relies heavily on human intuition about C-space structure at present. We discuss some of the challenges of parameter selection and convergence for applying this approach with an out-of-the box GAN and VAE model.