Pesquisa em Robótica NVIDIA

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 environments before deployment.

As robots tackle more complex tasks, traditional programming falls short. Reinforcement learning (RL) addresses this gap by training robots in virtual environments through trial and error—improving their skills in control, path planning, and manipulation.

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.