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GRAIL generates diverse humanoid manipulation and locomotion data using 3D assets and video foundation models, enabling effective sim-to-real transfer for humanoid robot control with high real-world success rates.
This paper presents a scalable open-source pipeline using NVIDIA NeMo for training and inference of Video Foundation Models, addressing challenges in generating high-quality videos with accelerated dataset curation and parallelized training.