让机器人自主实验成为发展中国家的科研基础设施
Infrastructure First: Enabling Embodied AI for Science in the Global South
- 以边缘计算、节能硬件等为基础构建可靠实验系统
- 在人力、电力、网络受限条件下实现持续实验
- 适合资源有限但想提升科研能力的机构
具身智能科学（EAI4S）通过融合感知、推理与机器人操作，使智能体能在真实世界中自主开展实验。对全球南方而言，这一转变并非追求先进自动化本身，而是突破核心能力瓶颈——实验人员严重不足。通过在人力、电力和连通性受限条件下实现持续可靠的实验，EAI4S将自动化从奢侈品转变为必要科研基础设施。主要障碍并非算法能力，而是基础设施。开源AI与基础模型缩小了知识差距，但EAI4S依赖可靠的边缘计算、能效硬件、模块化机器人系统、本地化数据管道与开放标准。缺乏这些基础，即使最先进的模型也困于资源丰富实验室。本文主张采取基础设施优先策略，明确大规模部署具身智能的实践要求，为全球南方机构提供将AI进展转化为可持续科研能力与竞争力的可行路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。