Solomon to integrate NVIDIA’s Isaac platform into its products

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At COMPUTEX 2024, Solomon's VP of Research & Development, Xuan-Loc Nguyen, introduces the META-aivi AR + AI vision system for SOP validation to visiting NVIDIA guests Madison Huang and Lori Huang.

At COMPUTEX 2024, Solomon VP of Research & Development Xuan-Loc Nguyen introduces the META-aivi AR + AI vision system for SOP validation to NVIDIA guests Madison Huang and Lori Huang. | Source: Solomon

Solomon Technology Corp. yesterday said it will integrate its product offerings with the NVIDIA Isaac robotics platform. The companies announced the collaboration at COMPUTEX 2024 in Taipei, Taiwan. 

“We are thrilled to integrate the NVIDIA Isaac platform into our products,” stated Johnny Chen, CEO of Solomon. “NVIDIA’s advanced AI and robotics tools will enhance our product capabilities in 3D machine vision, robotics control, and augmented intelligence, helping drive greater innovation in industrial automation.”

This collaboration will focus on integrating Solomon’s product offerings with the NVIDIA Isaac robotics platform. The partners said this will enhance Solomon’s 3D robotics vision and augmented intelligence systems. 

Founded in 1973, Solomon provides advanced vision systems, including 3D bin picking, vision-guided robots, AI-based defect inspection, and “augmented intelligence” blending artificial intelligence and augmented reality. The company said its embedded rapid AI model training, which allows users to customize models with minimal time investment, sets it apart from its competition.


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Solomon’s bin-picking system is a highlight of the collaboration

The global bin-picking system market was valued at $1.7 billion in 2023, according to Fact.MR. The market research firm predicted a compound annual growth rate (CAGR) of 12.5%, with the market reaching $5.6 billion by the end of 2033. Fact.MR noted that robotics and computer vision are the two primary technologies used in bin-picking applications. 

According to the collaborators Solomon’s bin-picking system will be enhanced by NVIDIA Isaac Manipulator accelerated libraries, which are based on NVIDIA Isaac ROS. Solomon said the bin-picking system will deliver eight times faster path planning and execution, as well as reduce path singularity occurrences by 50% in comparison with conventional algorithms. 

When added to the advanced image-recognition capability of Solmon’s AccuPick, these advancements will enable smaller robot cells without compromising cycle time, said the companies. NVIDIA and Solomon said this is essential for efficient bin picking in factories and order picking in logistics centers. 

“The era of AI robotics has arrived,” said Deepu Talla, vice president of robotics and edge computing at NVIDIA. “To meet this demand, NVIDIA is building a full-stack, accelerated robotics platform to enable ecosystem leaders such as Solomon to advance deployment of autonomous machines across the world’s largest industries.”

NVIDIA Isaac includes generative AI to offer foundational models for robotics. Solomon said it will continue delivering innovative products and applications by incorporating multiple NVIDIA Isaac technologies.

Solomon also said it plans to use this technology to bring smarter automation to manufacturing, retail, logistics, and other sectors. 

NVIDIA expands collaborations

Last month, RGo Robotics Inc. announced that it will integrate NVIDIA’s Isaac Robotics technology into its perception platforms. The company said the integration will “help advance AI-powered automation.”

RGo said its Perception Engine is an AI and vision system for localization, obstacle detection, and scene understanding. By combining it with NVIDIA Isaac Perceptor acceleration libraries, the companies said they will enable customers to deploy mobile robots within a few months.

The integrated software stack is compatible with the new NVIDIA Nova Orin Developer kit and is intended to ease setup for robots in changing environments, both indoors and outdoors, explained RGo. The company said its software helps robots learn on the go using computer vision, AI algorithms, and scalable sensor-fusion technology. 

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