Why ‘World Foundation Models’ are key to unlocking Physical AI and robotics

NVIDIA has unveiled Cosmos, a new platform featuring world foundation models (WFMs) designed to advance physical AI systems through enhanced environmental simulation capabilities.

The core technology: World foundation models are neural networks that can simulate physical environments and predict how scenes will evolve based on various inputs and actions.

• These models can generate detailed videos from text or image inputs while predicting scene evolution through a combination of current state data and control signals
• WFMs provide virtual 3D environments for testing AI systems without the risks and costs of real-world trials
• The technology enables the generation of synthetic training data, helping overcome the challenge of collecting vast amounts of real-world data

Platform specifics: NVIDIA Cosmos launches as an open platform with pre-trained models and essential development tools.

• The platform includes WFMs based on diffusion and auto-regressive architectures
• Built-in tokenizers can compress videos into tokens for transformer models
• The open architecture allows enterprises to build custom models or fine-tune existing ones based on specific needs

Key applications: Two major industries stand to benefit significantly from world foundation model development.

• Self-driving vehicles can undergo comprehensive testing in simulated environments featuring various weather conditions and traffic scenarios
• Humanoid robots can be safely tested and verified across different simulated environments before real-world deployment
• NVIDIA is partnering with companies like 1X, Huobi, and XPENG to advance physical AI development

Expert perspective: Ming-Yu Liu, NVIDIA’s vice president of research and IEEE Fellow, provides insight into the technology’s current state and future potential.

• Liu emphasizes that WFMs allow AI systems to imagine different environments and simulate future scenarios
• He acknowledges that while the technology is already useful, further development is needed to maximize its potential
• Integration methods between world models and physical AI systems require additional research and refinement

Future implications: The development of world foundation models represents an early stage in a broader evolution of physical AI systems.

• The technology could significantly reduce development costs and accelerate the deployment of safe, efficient AI systems
• Further research will likely focus on improving model accuracy and expanding simulation capabilities
• The open nature of the platform may foster innovation across multiple industries and applications