CTC and Macnica Launch "Physical AI Training"

ITOCHU Techno-Solutions Corporation (President: Tatsushi Shingu, Headquarters: Minato-ku, Tokyo, Abbreviation: CTC) and Macnica, Inc. (President: Kazumasa Hara, Headquarters: Yokohama City, Kanagawa Prefecture, hereinafter: Macnica) will begin offering "Physical AI Training" utilizing NVIDIA's platform.

This training package leverages NVIDIA Omniverse (a suite of libraries and development tools that support the construction, simulation, and operation of Physical AI applications) to provide comprehensive knowledge and skills necessary for building the foundation of "Physical AI" (AI that sees, judges, and moves in the real world). It covers everything from classroom lectures to environmental setup and practical exercises such as robot operation using actual machines. The training will primarily target the manufacturing industry, which is working on introducing Physical AI, with a goal of 20 orders within three years.

In Japan, labor shortages due to the declining birthrate and aging population, as well as skill transfer, are significant challenges. In industries such as manufacturing and logistics, AI that recognizes on-site conditions and acts autonomously is attracting attention as a technology that supports the reproduction of skilled techniques and automation. Unlike conventional AI that learns trends from data, Physical AI requires the reproduction 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 by integrating high-precision sensing of surroundings, learning algorithms, and feedback from actual machines.

The Physical AI Training is a comprehensive program where participants can learn Physical AI skills from basic to advanced levels, which are 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 such as motion control and sensor integration using edge devices and actual robots, participants can systematically acquire skills by iterating between virtual space and real machines. An option to provide GPU workstations to support the start of learning is also available.

This collaboration expands the training content that CTC has provided in digital twin environments by adding Macnica's robot execution environment technology and practical elements, making it possible to learn consistently from virtual to real machines. In addition, some curricula have been jointly developed by both companies to realize a more practical program from basic to advanced levels. Both companies, as NVIDIA certified partners, support companies in utilizing AI. CTC is responsible for the introduction and operation of Omniverse digital twins, while Macnica handles GPU infrastructure and robot technology, including NVIDIA Isaac.

Moving forward, both companies will continue to expand the curriculum in line with market needs and technological trends, contributing to the utilization of Physical AI and strengthening the competitiveness of companies as a foundational technology for the future.

■ Physical AI Training Image

■ 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.

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ITOCHU Techno-Solutions Corporation

https://www.ctc-g.co.jp/