  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.
