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

TL;DR

This paper introduces a real-time, sampling-free uncertainty estimation method for learning from demonstration using mixture density networks, enhancing safety in autonomous driving by effectively modeling complex human behaviors.

Contribution

It presents a novel uncertainty estimation technique with mixture density networks that operates without Monte Carlo sampling, suitable for real-time robotics applications.

Findings

Effective uncertainty distinction in synthetic scenarios

Improved safety in autonomous driving tasks

Sampling-free uncertainty estimation method

Abstract

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.