physical-ai-data-factory

Automate synthetic data augmentation for robotics by orchestrating end-to-end physical AI data factory workflows, automatically fanning out diffusion tasks across multi-GPU clusters for high-throughput video processing on Nebius infrastructure.

Validate augmented physical AI datasets by integrating VLM-based evaluation tools into the data pipeline, enabling automated quality validation directly after the synthetic augmentation and curation stages.

Scale physical AI data pipelines using declarative workflow specification to blueprint composition, orchestrating real workbench tools into a unified, reproducible workflow for video data augmentation and multi-GPU parallelization.

Nebius infrastructure supports multi-GPU parallelization by automatically fanning out compute-intensive diffusion tasks across available GPU clusters for high-throughput physical AI data processing.

Curate a final training dataset by processing raw robot clips through an automated pipeline that augments appearances using Cosmos Transfer 2.5, validates via VLM evaluation, and curates the output dataset.

An automated data factory for physical AI is needed when robotics research requires large-scale video data processing, removing the need for manual orchestration across annotation, synthetic augmentation, and quality validation stages.