Accelerating Robotic Innovation With Synthetic Data: Discover the Future at GTC

In today's data-driven world, the demand for high-quality AI training data is skyrocketing. Continued innovation in perception AI for robotics applications relies on large amounts of data to adequately prepare AI models for the real world.?

However, acquiring and labeling real-world data can be time-consuming, expensive, and sometimes limited in scope. This is where synthetic data generation comes into play, offering a game-changing way to develop better computer vision applications.

Synthetic data generation is becoming a vital step for a wide range of AI use cases, such as bootstrapping computer vision models and accelerating development of intelligent robots.

By integrating synthetic data generation into their existing robotics workflows, developers around the world have experienced significant benefits such as production efficiency, cost-effectiveness, and risk reduction. For example, Delta Electronics used synthetic data to accelerate the training of computer vision models for automated inspection, reducing production downtime and improving model accuracy.

At GTC 2024, leading companies will showcase how they have accelerated their AI development with synthetic data. Industry leaders and innovators will share how they are using synthetic data for unique use cases.

Generating Experiential Data for Humanoid Robots

Sanctuary AI simulates vision, audio, proprioception, and touch for robots synthetically with NVIDIA Omniverse. This synthetic data can be used to train large-scale generative models and then transferred from simulation to real robots performing automotive manufacturing tasks. Learn more and register.

Commissioning AI Vision Systems

Siemens is advancing the commissioning of AI vision systems by pre-training AI models entirely or primarily on synthetic data. With Universal Scene Description, also known as OpenUSD, these vision systems can be built, trained, and tested in a virtual world. Learn more and register.

Empowering Collaborative Robotics

Techman Robot is teaching robots to optimize motion trajectories, object recognition, defect identification, and other tasks through simulation in a digital environment. With visual recognition capabilities and built-in AI vision systems, collaborative robot arms can adapt to various product line configurations and expand the scope of what is currently possible in robotic positioning and inspection. Learn more and register.

Developing Industrial Machine Learning Models

Edge Impulse empowers engineers to train AI models at the edge for use cases such as warehouse asset tracking and vehicle detection with synthetic data. Using NVIDIA Omniverse Replicator, their developer base is generating highly realistic synthetic datasets tailored to computer vision models for unique industrial scenarios. Learn more and register.

Other Possibilities with Synthetic Data for Robotics

And that’s just the beginning. Synthetic data generation is bringing new possibilities for simulation, testing, and optimization. To discover the latest technologies that developers are building to generate synthetic data for computer vision workflows, register for GTC 2024.?

You can view sessions on synthetic data generation here and sessions on robotics here. Gain insights into the latest techniques, tools, and applications of synthetic data generation for computer vision applications, so you can find ways to accelerate AI model training, overcome data limitations, and improve model performance.

And at GTC, we’ll be hosting OpenUSD Day — join us to learn more about building generative AI-enabled 3D pipelines and tools using Universal Scene Description.