Showing posts tagged with: machine learning

Supply Chain Analytics

Artificial Intelligence, Cybernetics, eCommerce, Machine Learning, Neocortex, Robotic Order Fulfillment, Robotics, Robotics Business / 29.08.2014

Clint Reiser points out in the linked article that a new revolution in supply chain analytics is occurring. There is new level of data detail, it’s coming at us faster, providing bigger patterns and better insights.

The detail isn’t just point-of-sale information, customer buying patterns, or fleet telematics. From Universal’s perspective, the combining of machine learning and machine vision into robust automated solutions for material handling processes previously thought as random is a unique source of this data.

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Deep Learning for Robots

3D Vision Guidance, Artificial Intelligence, Cybernetics, Neocortex, Robotics, Robotics Business / 08.08.2014

Check out this interesting article on advances in deep learning for robots: Robots Helped Inspire Deep Learning and Might Become Its Killer App?

It’s worth comparing/contrasting between deep learning techniques and Neocortex. Neocortex is Universal Robotics’ patented machine learning platform, based on a seven-year development effort between NASA and Vanderbilt University. Even though the technology is currently employed on Robonaut 2 on the International Space Station, Universal’s focus with Neocortex is material handling tasks. By learning to recognize new objects or recognize previously seen objects that have changed, Neocortex machine intelligence brings flexibility to material handling automation.

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Universal Robotics Neocortex Shifts Material Handling Flexibility to Software

3D Vision Guidance, Artificial Intelligence, Machine Learning, Neocortex, Neocortex G2R Cell, Random Bin Picking, Robot Vision Guidance, Robotics / 05.08.2014

Check out this video showing Universal Robotics Neocortex – a patented next-generation machine intelligence software platform. It is guiding a robot to pick up a wide range of parts, providing flexible automation.

Traditionally, flexibility consisted of manually reconfiguring mechanical systems and sensors, and manually re-engineering algorithms to accommodate new parts. Neocortex’s machine learning can automatically handle a wide range of changing parts, reducing the need for manual changeovers. In the past, when a new part was introduced, even if the robot could pick up the new part, the robotic work cell would still require changes in fixturing, sensors, machine vision algorithms, and machine control.

In this video, see Neocortex guide the robot to handle various parts. Also shown is a demonstration of the simple training method for those occasional times an operator needs to teach Neocortex something new – a process that takes less than two minutes.

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