发表机构
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出机器人数据工厂（RDF），一种任务驱动的基础设施，通过持续生成、验证和重用机器人经验，实现部署-测量-学习-重复的闭环，以支持物理人工智能的可扩展发展。
AI 中文摘要
物理人工智能需要的不仅仅是日益庞大的机器人数据集：智能机器人通过与物理世界的持续交互来获取知识。我们认为，因此，物理人工智能的决定性科学资源不仅仅是原始机器人数据，而是机器人经验——物理上扎根的交互，其观察、动作、具身、上下文和结果保留了感知-行动-后果循环。我们引入了机器人数据工厂（RDF），这是一个任务驱动的基础设施和方法论，用于持续生成、验证、基准测试和重用此类经验。RDF通过可复现的任务、技能课程、同步多模态感知、外部真实数据、智能体机器人网络、数据管道和活基准来组织异构机器人和特定环境的训练场地。RDF不是将数据集视为静态的最终产品，而是实现了一个封闭的部署-测量-学习-重复循环，其中经过验证的物理经验支持世界模型、视觉-语言-动作模型、具身策略、数字孪生以及后续的机器人部署。我们进一步形式化了机器人经验及其质量，引入了任务-任务-技能-情节-数据集-基准-能力层级，并推导了定量缩放定律和算法合成程序，将机器人车队规模、传感器速率、存储、学习表示、分词、训练计算、推理和延迟与具身AI集群需求联系起来。该框架在三个互补的物理训练场地中实例化，分别用于家庭、环境和能源应用。因此，RDF将机器人数据生成重新定义为连续的科学生产过程，并为物理人工智能提供了通往可复现、可扩展以及最终联邦基础设施的途径。
英文摘要
Physical AI requires more than increasingly large robot datasets: intelligent robots acquire knowledge through continuous interaction with the physical world. We argue that the defining scientific resource of Physical AI is therefore not raw robot data alone, but robot experience - physically grounded interaction whose observations, actions, embodiment, context, and outcomes preserve the perception-action-consequence loop. We introduce the Robot Data Factory (RDF), a mission-driven infrastructure and methodology for continuously generating, validating, benchmarking, and reusing such experience. RDF organizes heterogeneous robots and environment-specific training grounds through reproducible missions, skill curricula, synchronized multimodal sensing, external ground truth, an agentic robot network, data pipelines, and living benchmarks. Rather than treating datasets as static end products, RDF implements a closed Deploy-Measure-Learn-Repeat cycle in which validated physical experience supports world models, vision-language-action models, embodied policies, digital twins, and subsequent robot deployment. We further formalize robot experience and its quality, introduce a mission-task-skill-episode-dataset-benchmark-capability hierarchy, and derive quantitative scaling laws and an algorithmic synthesis procedure connecting robot fleet size, sensor rates, storage, learning representations, tokenization, training compute, inference, and latency to Embodied-AI cluster requirements. The framework is instantiated in three complementary physical training grounds for domestic, environmental, and energy applications. RDF thus reframes robot data generation as a continuous scientific production process and provides a pathway toward reproducible, scalable, and eventually federated infrastructure for Physical AI.
