Accelerate Automation With NVIDIA Robotics

Preprogrammed robots struggle with unexpected changes, while AI-driven robots use simulation-based learning to adapt to dynamic environments. This lets them refine capabilities like navigation and manipulation, improving performance in a wide variety of scenarios.

Physical AI-powered robots need to autonomously perform complex tasks in dynamic environments. A "sim-first" approach is essential, allowing developers to train and validate these robots in physics-based digital twins before deployment.

Synthetic data addresses the challenge of data scarcity by providing text, videos, and images that can be used alongside real data to train multimodal physical AI models, saving time and reducing costs.

Virtual facilities—including factories, warehouses, distribution centers, semiconductor fabs, and data centers—unlock new possibilities for heavy industries. These virtual environments enable the design, simulation, operation, and optimization of assets and processes entirely in a digital space.