This revolutionary system, protected by DLRob's robust patent portfolio, represents a significant advancement in the field of autonomous robots.

Deep Learning Robotics (DLRob), a pioneering leader in artificial intelligence and robotics technology, announced in the framework of the Robot Tech Show 2023 last week the launch of its latest innovation, a feature in its vision-based controller that enables robots to autonomously pack and stack a wide range of objects with almost no setup time. This revolutionary system, protected by DLRob's robust patent portfolio, represents a significant advancement in the field of autonomous robots.

DLRob's AI robot controller is well known for enabling robots to learn from human demonstration. What sets this new feature apart is its ability to self-train using a unique combination of generated synthetic data and real performance data. By generating its synthetic data and merging it with its own real-world performance data, the vision-based controller achieves remarkable adaptability and accuracy in handling diverse objects and placing them in optimal locations with little or no setup time. This means that no user demonstration is needed.

"We are thrilled to introduce this new feature of our vision-based robot controller, which marks a major milestone in the field of AI-powered robots and automation," said Deep Learning Robotics' CEO. "By leveraging our self-training approach, the controller gains an unprecedented level of proficiency, enabling robots to pack and stack virtually anything by finding optimal locations for each of the objects identified in the scene. This breakthrough has the potential to transform various industries, from logistics and warehousing to manufacturing and beyond."

The new feature will be made available to a selected group of partners and clients in the upcoming months, with full availability planned for the next version of DLRob's controller.

DLRob's vision-based controller encompasses a multitude of cutting-edge technologies, including learning of tasks by observing end users' demonstrations, advanced human-robot interaction, and on-the-fly motion planning. The system intelligently analyses the shape and size of objects to optimize the packing and stacking process. Furthermore, it continuously learns from its own experiences, refining its performance over time to adapt to various scenarios and ensure consistent, error-free operations.

DLRob's commitment to intellectual property protection is reflected in its robust patent portfolio. The company's dedication to safeguarding its innovations reinforces its position as a leading provider of groundbreaking solutions in the robotics industry. By actively protecting its advancements, DLRob ensures that its customers benefit from the highest level of innovation and security.

With DLRob's vision-based controller, businesses can unlock unprecedented operational efficiency, maximize productivity, and reduce costs. The autonomous packing and stacking capabilities empower organizations to streamline their processes without robotics engineering efforts, and meet growing variability demands with unmatched precision and reliability.

DLRob invites industry professionals, researchers, and technology enthusiasts to witness the power of its vision-based AI robotic controller at upcoming conferences and exhibitions. The company remains committed to pushing the boundaries of AI-powered robots and continuing to revolutionize the industry worldwide.

For more information about DLRob and its solutions, please visit www.dlrob.com or contact info@dlrob.com.

About DLRob:

Deep Learning Robotics Ltd. is a leading provider of vision-based AI robotics software, empowering users to create and manage advanced robotic systems without technical training and in a plug & play manner. DLRob has been helping businesses across a range of industries to automate their operations and achieve greater efficiency and productivity where conventional technologies have not been able to do so. For more information, visit DLRob's website at www.dlrob.com.