Problem
Research questions and friction points this paper is trying to address.
Physical AI
Robot Experience
Perception-Action-Consequence Loop
Continuous Interaction
Reproducible Infrastructure
The Robot Data Factory
🤖 AI Summary
本文提出Robot Data Factory，通过持续生成、验证和重用机器人经验来解决物理AI中的知识获取问题，采用基础设施和方法论实现部署-测量-学习-重复的闭环。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Robot Data Factory
continuous scientific production process
Deploy-Measure-Learn-Repeat cycle
robot experience hierarchy
Embodied-AI cluster requirements
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