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Tsinghua AIBritish University in Egypt (BUE)National Institute of Clean-and-Low-CarbonNational Research Centre (NRC)SCUTShenzhen Institute of ArtificialApr 8, 2026arXiv:2604.06722This paper argues that deploying Embodied AI for Science (EAI4S) in the Global South hinges more on robust infrastructure than advanced AI algorithms. It identifies key infrastructure needs including dependable edge compute, energy-efficient hardware, modular robotics, and localized data pipelines. Prioritizing infrastructure enables continuous, reliable experimentation despite limitations in manpower, power, and connectivity, turning automation into essential scientific infrastructure.
Overcoming infrastructure limitations, not algorithmic capability, is the key to unlocking the potential of Embodied AI for Science in the Global South.
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.
