Uncertainty-Driven Control for a Self-Supervised Lifelong Learning Drone

Abstract

It has been a long standing goal of artificial intelligence to develop algorithms that support adaptive automation that allow unmanned vehicles to operate safely and independently in real-world environments. Here, we summarize experiments that demonstrate how a novel algorithm for measuring uncertainty during operation by a drone can support self-supervised learning. Our uncertainty-modulated learning algorithm is inspired by neuromodulatory mechanisms in the brain that control both the flow of information in neural circuits and the computational properties of those circuits. Our algorithm suggests how uncertainty can be used as an internal measure of performance that can trigger adaptation and the execution of information-seeking behaviors. This results in emergent behaviors that enable a drone to continually learn and adapt to support robust performance in real-world changing environments.