In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty acquisition can be done with a single forward path without Monte Carlo sampling and is suitable for real-time robotics applications. The properties of the proposed uncertainty measure are analyzed through three different synthetic examples, absence of data, heavy measurement noise, and composition of functions scenarios. We show that each case can be distinguished using the proposed uncertainty measure and presented an uncertainty-aware learn- ing from demonstration method of an autonomous driving using this property. The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.

本文介绍了一种利用混合密度网络进行不确定性感知的学习方法，能够模拟复杂且噪声干扰严重的人类行为；通过对人类行为数据进行不确定性分析，得出该方法能够有效应用于实时机器人应用中，特别在自动驾驶领域得到了安全性和性能上的提升。

利用无需采样的方差建模的混合密度网络从示范中学习不确定性感知