The paper introduces Ropedia Xperience 2, a large-scale dataset designed to establish a gold standard for scaling physical AI by jointly capturing environments and behaviors. The release comprises 1,589 metric-scale 3D scans and 4,230 egocentric recordings spanning 74 tasks over 131 hours. To enable closed-loop policy evaluation, each capture is systematically repurposed: scenes are converted into physically interactive simulation environments, while human motion data is retargeted to whole-body robot trajectories. The dataset is further augmented through virtual teleoperation and automated long-horizon trajectory generation to produce additional robot behaviors. This integrated pipeline—transforming static scans and human demonstrations into reusable, interactive assets—addresses the brittleness of foundation models like GPT-6 Astra and Claude Fable 5.1 on long-horizon, contact-rich tasks. By providing a continuous production and reuse framework for environment and behavior data, the work aims to lay the data foundation necessary for discovering scaling laws in physical AI analogous to those observed in language modeling.