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In this paper, we propose an uncertainty-aware learning from demonstrationmethod by presenting a novel uncertainty estimation method utilizing a mixturedensity network appropriate for modeling complex and noisy human behaviors. Theproposed uncertainty acquisition can be done with a single forward path withoutMonte Carlo sampling and is suitable for real-time robotics applications. Theproperties of the proposed uncertainty measure are analyzed through threedifferent synthetic examples, absence of data, heavy measurement noise, andcomposition of functions scenarios. We show that each case can be distinguishedusing the proposed uncertainty measure and presented an uncertainty-awarelearn- ing from demonstration method of an autonomous driving using thisproperty. The proposed uncertainty-aware learning from demonstration methodoutperforms other compared methods in terms of safety using a complexreal-world driving dataset.

Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling