Infrastructure First: Enabling Embodied AI for Science in the Global South
This work addresses the problem of limited scientific capacity due to manpower and resource shortages in the Global South, proposing an infrastructure-first approach to make EAI4S practical and scalable, though it is incremental as it builds on existing AI advances.
The paper tackles the challenge of deploying Embodied AI for Science (EAI4S) in the Global South by emphasizing infrastructure over algorithms, arguing that dependable edge compute, energy-efficient hardware, and open standards are essential to overcome capacity constraints and enable autonomous experimentation.
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.