Recorded talk

Techman AI Robot for Quality Inspection | Developed in Isaac Sim and powered by Omniverse

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Summary

Automated Optical Inspection (AOI) often needs access to product areas unreachable by fixed cameras, but programming articulated robot arms to capture hundreds of images is time-consuming and combinatorially complex. Techman built a digital twin in NVIDIA Omniverse using Isaac Sim to simulate and optimize robot poses and inspection sequences, and used Omniverse Replicator to synthesize training data for ML models. Simulation-driven optimization cut programming time by over 70% and reduced per-inspection cycle time by 20%; synthetic data dramatically improved model performance. The solution is deployed at the edge to run dozens of low-latency AI models during inspection, enabling faster, higher-quality AOI on production lines.

Key technical points

Context: AOI is used to find defects and ensure product quality across manufacturing lines.
Hardware approach: inspection cameras are mounted directly on articulated robotic arms to access areas fixed cameras cannot reach.
Programming challenge: determining precise arm positions and the most efficient sequence to capture potentially hundreds of images requires exploring thousands of possible paths.
Digital twin solution: Techman created a digital twin of the robot using NVIDIA Omniverse and Isaac Sim to emulate the physical system for development.
Simulation-based optimization: Isaac Sim’s optimization tools were used to explore all possible path options in simulation to find efficient inspection sequences.
Measured impact (development): using the Omniverse/Isaac Sim digital twin reduced programming time by over 70% versus manual operation of the real robot.
Measured impact (throughput): optimizing paths in simulation streamlined the cycle time of each inspection by 20%.
Synthetic data generation: Omniverse Replicator was used to produce high-quality synthetic datasets for training machine learning models.
Model improvement: models trained with the synthetic datasets showed dramatic performance improvements (as reported by Techman).
Edge deployment: the inspection stack is run at the edge, enabling dozens of AI models to execute efficiently with low latency during inspections.
Inputs (explicit): camera images captured by robot-mounted cameras, potentially hundreds per inspection session.
Outputs (explicit): optimized robotic paths and sequences, synthetic training datasets, and trained ML inspection models used during production inspections.
Evaluation methods (explicit): comparative metrics reported are programming-time reduction (>70%) and inspection cycle-time reduction (20%), obtained by simulation-driven optimization versus manual operation.
Workflow loop (implicit in source): simulate robot and paths in Omniverse → optimize sequences in Isaac Sim → generate synthetic data with Replicator to train models → deploy models and optimized paths to edge for live inspection.

Engineering relevance

For software and AI engineers, this demonstrates a simulation-first development loop: build a digital twin in Omniverse/Isaac Sim to accelerate robot path programming and explore large combinatorial path spaces with optimization tools rather than manual tuning on hardware. Use Omniverse Replicator to produce labeled synthetic datasets that significantly boost ML model performance, then deploy trained models and optimized motion plans to edge devices to run many low-latency inference pipelines in parallel. Key measurable outcomes to track are reductions in developer programming time (>70%) and per-inspection cycle time (20%), plus model accuracy improvements from synthetic data.

Japan relevance

Japanese manufacturers and robotics teams that prioritize high-quality inspection and tight production cycles can benefit from this approach: integrating robot-mounted cameras with a simulation-driven workflow reduces hands-on programming, shortens inspection cycle times, and improves defect detection via synthetic-data-trained models. Edge deployment of multiple low-latency models aligns with production requirements for reliable, real-time inspection on complex assemblies common in manufacturing sectors in Japan.

Full transcript

Transcript

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Automated Optical Inspection, or AOI, helps manufacturers identify defects and deliver high-quality products to their customers around the globe. Let's see how Techman, a subsidiary of Quanta, is using NVIDIA Isaac Sim to optimize inspection on their own manufacturing line. By placing the inspection camera directly on articulated robotic arms, Techman’s robotic AOI solutions can inspect areas of products that fixed cameras simply can't access. But programming the movement of these robots can be time-consuming. Developers need to determine the precise arm positions, as well as the most efficient sequence to capture potentially hundreds of images. This can involve exploring thousands of possibilities to determine an optimal solution. By building a digital twin of the robot using NVIDIA Omniverse and Isaac Sim, Techman was able to reduce the programming time by over 70% compared to manually operating the real robot. Then, using powerful optimization tools in Isaac Sim to explore all possible path options, they were able to streamline the cycle time of each inspection by 20 percent. To further improve the quality of the inspection, Techman employed Omniverse Replicator to generate high-quality, synthetic datasets. These were then used to train the machine learning models and dramatically enhanced model performance. And by running at the edge, dozens of AI models can be run – efficiently and with low latency – while inspecting particularly complicated products. Maximizing its use of NVIDIA Omniverse and AI, Techman's Robotics AOI solutions help its manufacturing customers speed up inspections while improving product quality.