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

• URL: http://arxiv.org/abs/2604.06722v1
• Date: Wed, 08 Apr 2026 06:39:25 GMT
• Title: Infrastructure First: Enabling Embodied AI for Science in the Global South
• Abstract summary: Embodied AI for Science (EAI4S) brings intelligence into the laboratory by uniting perception, reasoning, and robotic action.<n>This article argues for an infrastructure-first approach to EAI4S and outlines the practical requirements for deploying embodied intelligence at scale.
• Score: 16.251692668102784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Software Engineering for AI-driven Building Operation [0.0]
  Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings reveals a significant software engineering (SE) challenge.<n>Although actual safety-critical failures are rare, the physical and lasting nature of even minor failures fundamentally changes SE4AI requirements.<n> Rooted in two interdisciplinary research projects in civil engineering and computer science that target the AI-driven optimization of building operations, we identify the missing perspectives in SE4AI that currently stymie the successful deployment of AI-based systems for building operations.
  arXiv  Detail & Related papers  (2026-08-17T08:14:39Z)
• The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI [2.134211474877041]
  Recent evidence shows that generative AI disproportionately affects highly educated, white collar work.<n>This paper proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy.<n>The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work.
  arXiv  Detail & Related papers  (2026-01-10T09:27:56Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• A roadmap for AI in robotics [55.87087746398059]
  We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
  arXiv  Detail & Related papers  (2025-07-26T15:18:28Z)
• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
  This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
  arXiv  Detail & Related papers  (2025-05-13T17:59:20Z)
• Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF [1.952039268768816]
  Deep learning methods are playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics.<n>This white paper provides a strategic roadmap, informed by a community survey, to address these barriers.<n>It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.
  arXiv  Detail & Related papers  (2025-03-18T12:11:11Z)
• AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge [4.055489363682199]
  We introduce the concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science. Our goal is to ensure the natural consequence of AI4Science (e.g., self-driving labs) is a benefit not only for its developers but for society as a whole.
  arXiv  Detail & Related papers  (2023-10-28T23:57:15Z)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
  New area of research known as AI for science (AI4Science)<n>Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.<n>Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
  arXiv  Detail & Related papers  (2023-07-17T12:14:14Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.