Selected Trends in Artificial Intelligence for Space Applications

TL;DR

This paper reviews emerging AI trends in space applications, focusing on differentiable intelligence and onboard machine learning, highlighting innovative projects from ESA that go beyond traditional AI methods.

Contribution

It identifies and discusses two key emerging AI trends in space, providing insights into advanced ESA projects that push beyond conventional approaches.

Findings

Differentiable intelligence leverages automatic differentiation for learning.

Onboard machine learning enables inference and learning directly in space.

ESA projects exemplify cutting-edge AI applications in space.

Abstract

The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced. As more and more aerospace engineers are becoming aware of new trends in AI, traditional approaches are revisited to consider the applications of emerging AI technologies. Already at the time of writing, the scope of AI-related activities across academia, the aerospace industry and space agencies is so wide that an in-depth review would not fit in these pages. In this chapter we focus instead on two main emerging trends we believe capture the most relevant and exciting activities in the field: differentiable intelligence and on-board machine learning. Differentiable intelligence, in a nutshell, refers to works making extensive use of automatic differentiation frameworks to learn the parameters of machine learning or related models. Onboard machine learning considers the problem of moving inference, as well as learning, onboard. Within these fields, we discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT), giving priority to advanced topics going beyond the transposition of established AI techniques and practices to the space domain.