The blueprint for testing robot fleets in digital twins is now available in preview
Advances in physical AI are enabling organizations to embrace embodied AI across their operations, bringing unprecedented intelligence, automation and productivity to the world’s factories, warehouses and industrial facilities.
Humanoid robots can work alongside human teams, autonomous mobile robots (AMRs) can navigate complex warehouse environments, and intelligent cameras and visual AI agents can monitor and optimize entire facilities. In these ways, physical AI is becoming integral to today’s industrial operations.
Helping industrial enterprises accelerate the development, testing and deployment of physical AI, the Mega NVIDIA Omniverse Blueprint for testing multi-robot fleets in digital twins is now available in preview on build.nvidia.com.
At Hannover Messe — a trade show on industrial development running through April 4 in Germany — manufacturing, warehousing and supply chain leaders such as Accenture and Schaeffler are showcasing their adoption of the blueprint to simulate Digit, a humanoid robot from Agility Robotics, and discussing how they use industrial AI and digital twins to optimize facility layouts, material flow and collaboration between humans and robots inside complex production environments.
In addition, NVIDIA ecosystem partners — including Delta Electronics, Rockwell Automation and Siemens — are announcing further integrations with NVIDIA Omniverse and NVIDIA AI technologies at the event.
Digital Twins — the Training Ground for Physical AI
Industrial facility digital twins are physically accurate virtual replicas of real-world facilities that serve as critical testing grounds for simulating and validating physical AI and how robots and autonomous fleets interact, collaborate and tackle complex tasks before deployment.
Developers can use NVIDIA Omniverse platform technologies and the Universal Scene Description (OpenUSD) framework to develop digital twins of their facilities and processes. This simulation-first approach dramatically accelerates development cycles while reducing the costs and risks associated with real-world testing.
Built for a Diversity of Robots and AI Agents
The Mega blueprint equips industrial enterprises with a reference workflow for combining sensor simulation and synthetic data generation to simulate complex human-robot interactions and verify the performance of autonomous systems in industrial digital twins.
Enterprises can use Mega to test various robot brains and policies at scale for mobility, navigation, dexterity and spatial reasoning. This enables fleets comprising different types of robots to work together as a coordinated system.
As robot brains execute their missions in simulation, they perceive the results of their actions through sensor simulation and plan their next action. This cycle continues until the policies are refined and ready for deployment.
Once validated, these policies are deployed to real robots, which continue to learn from their environment — sending sensor information back through the entire loop and creating a continuous learning and improvement cycle.
Transforming Industrial Operations With Visual AI Agents
In addition to AMRs and humanoid robots, advanced visual AI agents extract information from live and recorded video data, enabling new levels of intelligence and automation. These visual AI agents bring real-time contextual awareness to robots and help to improve worker safety, maintain warehouse compliance, support visual inspection and maximize space utilization.
To support developers building visual AI agents, which can be integrated with the Mega blueprint, NVIDIA last year announced an AI Blueprint for video search and summarization (VSS). At Hannover Messe, leading partners are featuring how they use the VSS blueprint to improve productivity and operational efficiency.
