Overview
Physical AI for End-to-End Development
Asphysical AI-powered edge systems and infrastructure increasingly automate, they must autonomously perceive, plan, and execute complex tasks—from traffic pattern detection and industrial inspection to autonomous mobile robots in warehouses and logistics.
To develop and deploy the next generation of autonomous AI systems, a new framework is required. This involves training multimodal, generalized AI models forvarious tasks, then testing and validating these models and their associated software insimulation. Finally, the entire stack is deployed on the physical edge AI system to perform actions in real time.
NVIDIA’s three computers—for training, simulation, and deployment—are essential for achieving human-like intelligence for autonomousedge solutions.
The Three-Computer Solution: Powering the Next Wave of AI Robotics
Industrial and physical AI systems, from humanoids to factories, are accelerated by NVIDIA’s three computers for training, simulation, and inference.
European Robot Makers Adopt NVIDIA Isaac, Omniverse and Halos to Develop Safe, Physical AI-Driven Robot Fleets
At GTC Paris, Europe’s leading robot developers are showcasing their latest AI-driven robots and automation breakthroughs, all accelerated by NVIDIA technologies.
Solutions
Explore Our Robotics and Edge AI Solutions
Robotics
- Accelerate robotics with AI—from development to simulation to deployment.
- Enable key robotic functions: mobility, grasping, navigation, functional safety, and perception.
- Build robots across industries, including manufacturing, retail, agriculture, logistics, delivery, healthcare, and more.
Vision AI
Train, build, deploy, and scale vision AI applications from edge to cloud.
Unlock valuable insights for many spaces, including retail, warehouses, cities, and more.
Bring visual data and AI together to improve efficiency and safety in multiple industries.
Edge AI
- Bring the power of AI to edge devices and process data at the source.
- Discover actionable, real-time insights to make better decisions, improve services, and streamline operations.
- Improve security and reduce costs through local processing.
Resources
The Latest in Robotics & Edge AI Resources
- What's New
- Use Cases
- Videos
Humanoid Robots
General-purpose humanoid robots are designed to adapt quickly to human-centric environments, handling tedious or physically demanding tasks. They are now being used in factories and healthcare facilities to assist humans and address labor shortages.
Synthetic Data Generation
Training AI models is often hampered by limited or expensive real-world data. Synthetic data, generated through simulations or AI, can significantly reduce training time and costs while improving model performance.
Virtual Facility Digital Twins
Virtual facilities, including factories, warehouses and distribution centers, semiconductor fabs, and data centers, unlock new possibilities for the world’s heavy industries, allowing them to design,simulate, operate, and optimize their assets and processes—entirely virtually.
AI-Powered Multi-Camera Tracking
In a future where factories, retail spaces, and public areas operate efficiently and safely, multi-camera tracking uses hundreds of cameras to accurately monitor and manage large areas by following objects and measuring activity.
Generative AI-Powered Visual AI Agents
Traditional video analytics use fixed models limited to detecting predefined objects. Generative AI and foundation models enable more complex, broad perception and contextual understanding, creating smarter video analytics AI agents.
Robotics Simulation
Physical AI-powered robots must autonomously sense, plan, and perform complex tasks in dynamic environments. A "sim-first" approach, using robot simulation in digital environments, is essential for training and validating these systems before deployment.
Introducing NVIDIA DGX Spark
DGX Spark brings the power of NVIDIA Grace Blackwell™ to developer desktops. The GB10 Superchip, combined with 128 GB of unified system memory, lets AI researchers, data scientists, and students work with AI models locally with up to 200 billion parameters.