ロボットを信頼せよ！スパースガウス過程によるニューラルネットワークの予測不確実性推定
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
DNNをスパースガウス過程の混合専門家として理論的に位置づけ、ロボットタスクで高速かつ信頼性の高い予測不確実性推定を実現した研究。
著者: Jongseok Lee, Jianxiang Feng, Matthias Humt, Marcus G. Müller, Rudolph Triebel
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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.
