Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Abstract
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a practical and principled combination of DNNs with sparse Gaussian Processes (GPs). We prove theoretically that DNNs can be seen as a special case of sparse GPs, namely mixtures of GP experts (MoE-GP), and we devise a learning algorithm that brings the derived theory into practice. In experiments from two different robotic tasks -- inverse dynamics of a manipulator and object detection on a micro-aerial vehicle (MAV) -- we show the effectiveness of our approach in terms of predictive uncertainty, improved scalability, and run-time efficiency on a Jetson TX2. We thus argue that our approach can pave the way towards reliable and fast robot learning systems with uncertainty awareness.
Cite
@article{arxiv.2109.09690,
title = {Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes},
author = {Jongseok Lee and Jianxiang Feng and Matthias Humt and Marcus G. Müller and Rudolph Triebel},
journal= {arXiv preprint arXiv:2109.09690},
year = {2021}
}
Comments
12 pages, 6 figures and 1 table. Accepted at the 5th Conference on Robot Learning (CORL 2021), London, UK