NVIDIA, 피지컬 AI용 Cosmos 월드 기초 모델 대규모 출시
무슨 발표인가
- —커스터마이징 가능한 추론 모델로 피지컬 AI 개발 지원
- —1X·Agility Robotics·Figure AI·Foretelrix·Skild AI·Uber 얼리 어댑터
- —로봇·자율주행 훈련 데이터 합성 생성 엔진
원문 (영어)
GTC — NVIDIA today announced a major release of new NVIDIA Cosmos world foundation models (WFMs), introducing an open and fully customizable reasoning model for physical AI development and giving developers unprecedented control over world generation.
NVIDIA is also launching two new blueprints — powered by the NVIDIA Omniverse and Cosmos platforms — that provide developers with massive, controllable synthetic data generation engines for post-training robots and autonomous vehicles. Industry leaders including 1X, Agility Robotics , Figure AI, Foretellix, Skild AI and Uber are among the first to adopt Cosmos to generate richer training data for physical AI faster and at scale.
“Just as large language models revolutionized generative and agentic AI, Cosmos world foundation models are a breakthrough for physical AI,” said Jensen Huang, founder and CEO of NVIDIA. “Cosmos introduces an open and fully customizable reasoning model for physical AI and unlocks opportunities for step-function advances in robotics and the physical industries.”
Cosmos Transfer for Synthetic Data Generation Cosmos Transfer WFMs ingest structured video inputs such as segmentation maps, depth maps, lidar scans, pose estimation maps and trajectory maps to generate controllable photoreal video outputs. Cosmos Transfer streamlines perception AI training, transforming 3D simulations or ground truth created in Omniverse into photorealistic videos for large-scale, controllable synthetic data generation .
Agility Robotics will be an early adopter of Cosmos Transfer and Omniverse for large-scale synthetic data generation to train its robot models .
원문: NVIDIA News — "NVIDIA Announces Major Release of Cosmos World Foundation Models and Physical AI Data Tools" (2025-07-03) 공식 원문: https://nvidianews.nvidia.com/news/nvidia-announces-major-release-of-cosmos-world-foundation-models-and-physical-ai-data-tools