Deep Learning based Uncertainty Decomposition for Real-time Control

TL;DR

This paper introduces a deep learning method for real-time uncertainty decomposition, distinguishing between aleatoric and epistemic uncertainties to improve safety and exploration in data-driven control systems.

Contribution

It presents a novel continuous scalar detector for epistemic uncertainty, enabling sample-free, real-time uncertainty estimation that enhances control strategies in unknown environments.

Findings

The proposed detector outperforms existing methods on synthetic and real datasets.

It enables real-time, sample-free uncertainty estimation.

Demonstrated effective online control of a simulated quadcopter under unknown disturbances.

Abstract

Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty that arises from measurement noise can often be explicitly modeled given a parametric description, it can be harder to model epistemic uncertainty, which describes the presence or absence of training data. The latter can be particularly useful for implementing exploratory control strategies when system dynamics are unknown. We propose a novel method for detecting the absence of training data using deep learning, which gives a continuous valued scalar output between $0$ (indicating low uncertainty) and $1$ (indicating high uncertainty). We utilize this detector as a proxy for epistemic uncertainty and show its advantages over existing approaches on synthetic and real-world datasets. Our approach can be directly combined with aleatoric uncertainty estimates and allows for uncertainty estimation in real-time as the inference is sample-free unlike existing approaches for uncertainty modeling. We further demonstrate the practicality of this uncertainty estimate in deploying online data-efficient control on a simulated quadcopter acted upon by an unknown disturbance model.