CTC and Macnica Launch "Physical AI Training"
ITOCHU Techno-Solutions Corporation (CTC) and Macnica, Inc. have jointly launched "Physical AI Training," leveraging NVIDIA's platform. This comprehensive program aims to equip manufacturing industries with the essential knowledge and skills for building Physical AI foundations through both theoretical learning and practical exercises using real robots and simulations.
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- 📰 Published: May 13, 2026 at 19:00
- 🔍 Collected: May 13, 2026 at 10:31
- 🤖 AI Analyzed: May 13, 2026 at 11:43 (1h 11m after Collected)
ITOCHU Techno-Solutions Corporation (President & CEO: Tatsushi Shingu, Headquarters: Minato-ku, Tokyo, Abbreviation: CTC) and Macnica, Inc. (President & CEO: Kazumasa Hara, Headquarters: Yokohama, Kanagawa, hereinafter: Macnica) will begin offering "Physical AI Training" utilizing NVIDIA's platform.
This training package comprehensively covers the knowledge and skills necessary for building the foundation of "Physical AI" (AI that sees, judges, and moves in the real world) by utilizing NVIDIA Omniverse (a suite of libraries and development tools that support the construction, simulation, and operation of physical AI applications). It includes theoretical learning, environment setup, and practical exercises such as robot operation using actual machines. The program will be primarily rolled out to the manufacturing industry, which is actively engaged in adopting Physical AI, with a goal of 20 orders within three years.
In Japan, a shrinking birthrate and aging population have led to challenges such as labor shortages and difficulties in technical succession. In industries like manufacturing and logistics, AI capable of recognizing on-site conditions and acting autonomously is drawing attention as a technology that supports the replication of skilled expertise and automation. Unlike traditional AI that learns patterns from data, Physical AI requires the replication of skills based on the five senses – seeing, touching, and judging. This necessitates the construction of a virtual space (digital twin technology) that precisely reproduces physical behavior, integrating high-precision sensing of the surroundings, learning algorithms, and feedback from actual machines.
Physical AI Training is a comprehensive program designed to teach the essential Physical AI skills, from foundational knowledge to advanced applications, indispensable for robot operation, automation, and autonomous AI. Participants will learn how to develop applications using NVIDIA Omniverse and NVIDIA AI Enterprise, a software suite for AI development.
In this training, after learning the basic knowledge of Physical AI, participants will simulate robot behavior in a digital twin environment using NVIDIA Omniverse. Furthermore, through practical exercises involving motion control and sensor integration with edge devices and actual robots, participants can systematically acquire skills by moving back and forth between virtual spaces and real machines. An optional GPU workstation provision is also available to support the start of learning.
This collaboration expands CTC's existing digital twin environment training by adding Macnica's expertise in robot execution environments and practical exercise elements, creating a curriculum that allows for consistent learning from virtual to real machines. Additionally, some curricula have been jointly developed by both companies, realizing a program that enables more practical learning from basic to advanced levels. As NVIDIA certified partners, both companies support the utilization of AI in enterprises; CTC handles the introduction and operation of Omniverse digital twins, while Macnica is responsible for GPU infrastructure and robot technologies, including NVIDIA Isaac.
Moving forward, both companies will continue to expand their curriculum in response to market needs and technological trends, contributing to the utilization of Physical AI and the strengthening of competitiveness for companies as a foundational technology supporting the future.
■ Physical AI Training Image Diagram
■ Sites
https://www.ctc-g.co.jp/solutions/nvidia/omniverse.html
https://www.macnica.co.jp/business/semiconductor/manufacturers/nvidia/products/143448/
https://www.ctc-g.co.jp/solutions/nvidia/ai_enterprise.html
https://www.macnica.co.jp/business/semiconductor/manufacturers/nvidia/products/143447/
* Product names and other proper nouns mentioned are trademarks or registered trademarks of their respective companies.
* The information published is current as of the announcement date. Please note that the latest information may differ.
End of document.
This training package comprehensively covers the knowledge and skills necessary for building the foundation of "Physical AI" (AI that sees, judges, and moves in the real world) by utilizing NVIDIA Omniverse (a suite of libraries and development tools that support the construction, simulation, and operation of physical AI applications). It includes theoretical learning, environment setup, and practical exercises such as robot operation using actual machines. The program will be primarily rolled out to the manufacturing industry, which is actively engaged in adopting Physical AI, with a goal of 20 orders within three years.
In Japan, a shrinking birthrate and aging population have led to challenges such as labor shortages and difficulties in technical succession. In industries like manufacturing and logistics, AI capable of recognizing on-site conditions and acting autonomously is drawing attention as a technology that supports the replication of skilled expertise and automation. Unlike traditional AI that learns patterns from data, Physical AI requires the replication of skills based on the five senses – seeing, touching, and judging. This necessitates the construction of a virtual space (digital twin technology) that precisely reproduces physical behavior, integrating high-precision sensing of the surroundings, learning algorithms, and feedback from actual machines.
Physical AI Training is a comprehensive program designed to teach the essential Physical AI skills, from foundational knowledge to advanced applications, indispensable for robot operation, automation, and autonomous AI. Participants will learn how to develop applications using NVIDIA Omniverse and NVIDIA AI Enterprise, a software suite for AI development.
In this training, after learning the basic knowledge of Physical AI, participants will simulate robot behavior in a digital twin environment using NVIDIA Omniverse. Furthermore, through practical exercises involving motion control and sensor integration with edge devices and actual robots, participants can systematically acquire skills by moving back and forth between virtual spaces and real machines. An optional GPU workstation provision is also available to support the start of learning.
This collaboration expands CTC's existing digital twin environment training by adding Macnica's expertise in robot execution environments and practical exercise elements, creating a curriculum that allows for consistent learning from virtual to real machines. Additionally, some curricula have been jointly developed by both companies, realizing a program that enables more practical learning from basic to advanced levels. As NVIDIA certified partners, both companies support the utilization of AI in enterprises; CTC handles the introduction and operation of Omniverse digital twins, while Macnica is responsible for GPU infrastructure and robot technologies, including NVIDIA Isaac.
Moving forward, both companies will continue to expand their curriculum in response to market needs and technological trends, contributing to the utilization of Physical AI and the strengthening of competitiveness for companies as a foundational technology supporting the future.
■ Physical AI Training Image Diagram
■ Sites
https://www.ctc-g.co.jp/solutions/nvidia/omniverse.html
https://www.macnica.co.jp/business/semiconductor/manufacturers/nvidia/products/143448/
https://www.ctc-g.co.jp/solutions/nvidia/ai_enterprise.html
https://www.macnica.co.jp/business/semiconductor/manufacturers/nvidia/products/143447/
* Product names and other proper nouns mentioned are trademarks or registered trademarks of their respective companies.
* The information published is current as of the announcement date. Please note that the latest information may differ.
End of document.