{"id":4233,"date":"2025-08-20T13:41:04","date_gmt":"2025-08-20T13:41:04","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2025\/08\/20\/into-the-omniverse-how-openusd-and-digital-twins-are-powering-industrial-and-physical-ai\/"},"modified":"2025-08-20T13:41:04","modified_gmt":"2025-08-20T13:41:04","slug":"into-the-omniverse-how-openusd-and-digital-twins-are-powering-industrial-and-physical-ai","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2025\/08\/20\/into-the-omniverse-how-openusd-and-digital-twins-are-powering-industrial-and-physical-ai\/","title":{"rendered":"Into the Omniverse: How OpenUSD and Digital Twins Are Powering Industrial and Physical AI"},"content":{"rendered":"
\n\t\t<\/span> <\/span> <\/span><\/span><\/p>\n

Editor\u2019s note: This blog is a part of <\/i>Into the Omniverse<\/i><\/a>, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advances in <\/i>OpenUSD<\/i><\/a> and <\/i>NVIDIA Omniverse<\/i><\/a>.<\/i><\/p>\n

Investments in industrial AI<\/a> and physical AI<\/a> are driving increased demand for digital twins<\/a> across industries.<\/p>\n

These physically accurate, virtual replicas of real-world environments, facilities and processes aren\u2019t just helping manufacturers streamline planning and optimize operations. They serve as the training ground for helping ensure vision AI agents, autonomous vehicles and robot fleets can operate safely, efficiently and reliably.<\/p>\n

Creating physically accurate simulation environments that enable physical AI to transition seamlessly to the real world typically involves substantial manual effort. However, with the latest advancements in OpenUSD \u2014 a powerful open standard for describing and connecting complex 3D worlds \u2014 alongside improvements in rendering, neural reconstruction and world foundation models (WFMs)<\/a>, developers can fast-track the construction of digital twins at scale.<\/p>\n

Accelerating Digital Twin and Physical AI Development<\/b><\/h2>\n

To speed digital twin and physical AI development, NVIDIA announced at this year\u2019s SIGGRAPH conference new research, NVIDIA Omniverse libraries, NVIDIA Cosmos WFMs and advanced AI infrastructure \u2014 including NVIDIA RTX PRO Servers<\/a> and NVIDIA DGX Cloud<\/a>.<\/p>\n<\/p>\n

Growing OpenUSD Ecosystem<\/b><\/h2>\n

OpenUSD serves as a foundational ecosystem for digital twin and physical AI development, empowering developers to integrate industrial and 3D data to create physically accurate digital twins.<\/p>\n

The Alliance for OpenUSD<\/a> (AOUSD) recently welcomed new general members<\/a>, including Accenture, Esri, HCLTech, PTC, Renault and Tech Soft 3D. These additions underscore the continued growth of the OpenUSD community and its commitment to unifying 3D workflows across industries.<\/p>\n

To address the growing demand for OpenUSD and digital twins expertise, NVIDIA launched a new industry-recognized OpenUSD development certification<\/a> and a free digital twins learning path<\/a>.<\/p>\n

Developers Building Digital Twins<\/b><\/h2>\n

Industry leaders including Siemens<\/a>, Sight Machine<\/a>, Rockwell Automation<\/a>, EDAG<\/a>, Amazon Devices & Services<\/a> and Vention<\/a> are building digital twin solutions with Omniverse libraries and OpenUSD to enable transformation with industrial and physical AI.<\/p>\n

Siemens<\/a>\u2019 Teamcenter Digital Reality Viewer enables engineers to visualize, interact with and collaborate on photorealistic digital twins at unprecedented scale. These efforts are enabling faster design reviews, minimizing the need for physical prototypes and accelerating time to market \u2014 all while reducing costs.<\/p>\n

\"\"<\/p>\n

Sight Machine\u2019s<\/a> Operator Agent platform combines live production data, agentic AI-powered recommendations and digital twins to provide real-time visibility into production and enable faster, more informed decisions for plant operations teams.<\/p>\n

\"\"<\/p>\n

Rockwell Automation\u2019s<\/a> Emulate3D Factory Test platform enables manufacturers to build factory-scale, physics-based digital twins for simulating, validating and optimizing automation and autonomous systems at scale.<\/p>\n

\"\"<\/p>\n

EDAG\u2019s<\/a> industrial digital twin platform helps manufacturers improve project management, optimize production layouts, train workers and perform data-driven quality assurance.\u00a0Amazon Devices & Services<\/a> uses digital twins to train robotic arms to recognize, inspect and handle new devices. Robotic actions can be configured to manufacture products purely based on training performed in simulation \u2014 including for steps involved in assembly, testing, packaging and auditing.<\/p>\n<\/p>\n

Vention<\/a> is using NVIDIA robotics, AI and simulation technologies \u2014 including Omniverse libraries, Isaac Sim and Jetson hardware \u2014 to deliver plug-and-play digital twin and automation solutions that simplify and accelerate the deployment of intelligent manufacturing systems.<\/p>\n

Get Plugged Into the World of OpenUSD<\/b><\/h2>\n

To learn more about OpenUSD and how to develop digital twin applications with Omniverse libraries, take free courses as part of the new digital twin learning path<\/a>, and check out the Omniverse Kit companion tutorial<\/a> and how-to guide for deploying Omniverse Kit-based applications at scale<\/a>.<\/p>\n

Watch a replay of NVIDIA\u2019s SIGGRAPH Research Special Address<\/a>. Plus, try out Omniverse NuRec on Isaac Sim and CARLA<\/a>, and learn more about Isaac Sim<\/a>.<\/i><\/p>\n

Stay up to date by subscribing to<\/i> NVIDIA Omniverse news<\/i><\/a>, joining the Omniverse <\/i>community<\/i><\/a> and following Omniverse <\/i>on<\/i> Discord<\/i><\/a>,<\/i> Instagram<\/i><\/a>, <\/i>LinkedIn<\/i><\/a>, <\/i>Threads<\/i><\/a>,<\/i><\/a> X<\/i><\/a>, <\/i>and<\/i> YouTube<\/i><\/a>.\u00a0<\/i><\/b><\/p>\n

Explore the <\/i>Alliance for OpenUSD forum<\/i><\/a> and the <\/i>AOUSD website<\/i><\/a>.<\/i><\/p>\n

Featured image courtesy of Siemens, Sight Machine.<\/span><\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

https:\/\/blogs.nvidia.com\/blog\/openusd-digital-twins-industrial-physical-ai\/<\/p>\n","protected":false},"author":0,"featured_media":4234,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4233"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=4233"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4233\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/4234"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=4233"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=4233"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=4233"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}