Infrastructure First: Enabling Embodied AI for Science in the Global South

TL;DR

This paper advocates for an infrastructure-first approach to embodied AI for science in the Global South, emphasizing the need for reliable hardware, data pipelines, and standards to enable autonomous experimentation.

Contribution

It highlights the critical infrastructure requirements for deploying embodied AI in resource-limited settings and provides practical guidelines for scaling scientific automation.

Findings

Dependable edge compute and energy-efficient hardware are essential.

Open standards and modular robotic systems facilitate scalable deployment.

Infrastructure constraints are the main barrier, not algorithmic capability.

Abstract

Embodied AI for Science (EAI4S) brings intelligence into the laboratory by uniting perception, reasoning, and robotic action to autonomously run experiments in the physical world. For the Global South, this shift is not about adopting advanced automation for its own sake, but about overcoming a fundamental capacity constraint: too few hands to run too many experiments. By enabling continuous, reliable experimentation under limits of manpower, power, and connectivity, EAI4S turns automation from a luxury into essential scientific infrastructure. The main obstacle, however, is not algorithmic capability. It is infrastructure. Open-source AI and foundation models have narrowed the knowledge gap, but EAI4S depends on dependable edge compute, energy-efficient hardware, modular robotic systems, localized data pipelines, and open standards. Without these foundations, even the most capable models remain trapped in well-resourced laboratories. This article argues for an infrastructure-first approach to EAI4S and outlines the practical requirements for deploying embodied intelligence at scale, offering a concrete pathway for Global South institutions to translate AI advances into sustained scientific capacity and competitive research output.