BINYAMINA, Israel - iSportsWire -- Deep Learning Robotics (DLR), a leading innovator in the field of robotics and artificial intelligence, announced at the AI Week in Tel-Aviv the grant of a new patent for a cutting-edge technology enabling the training of objects' visual and 3D features to computer vision models with the assistance of robotic devices.

It is clear today that high-quality training data is crucial for computer vision deep learning models and AI. Back in 2016 we pioneered the usage of robotic devices to create such data by generating and linking visual and spatial features of objects automatically and in real-time, leading to more accurate models and faster deployment to robotic vision systems.

According to Carlos Benaim, the CEO of DLR, this novel technology has the potential to revolutionize the existing techniques of generating 3D data for training computer vision models. Furthermore, it will facilitate the creation of robot applications capable of accurately identifying and locating objects in a given scene, even when using basic 2D cameras.

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DLR is committed to pushing the boundaries of what is possible with robotics and artificial intelligence, and this new patent is a testament to that commitment. The company is excited to continue to explore the full potential of this technology and bring new solutions to market in the years to come.

About Deep Learning Robotics (DLR)
Deep Learning Robotics is a prominent player in the domain of robotics and artificial intelligence, dedicated to creating ground-breaking solutions applied to automation. The company has developed a unique vision-based robotic controller that uses advanced machine learning algorithms to enable a robot to learn and replicate tasks by observing humans performing them. With a team of experts in robotics, computer vision, and machine learning, the company is dedicated to advancing the state of the art in these fields and bringing new solutions to market.
For more information, please contact:
info@dlrob.com
https://www.dlrob.com
Patents granted: US10974394B2 and EP3458919B1.