AI-Driven Bin Picking Detects Subtle Differences

Apera’s Sina Afrooze on machine learning that improves part handling performance.

Object recognition in bin picking and robotic assembly is a surprisingly difficult problem, a problem that is exponentially more difficult when the difference between objects is subtle. Machine learning promises to revolutionize the training of assembly and sorting robots iteratively, allowing high-speed machines to differentiate objects that are almost identical. At Automate 2023, Apera Co-founder and CEO Sina Afrooze shows Jim Anderton a system that can separate similar objects in a common industrial situation: fine and coarse thread pitch fasteners, mixed together.  

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Written by

James Anderton

Jim Anderton is the Director of Content for Mr. Anderton was formerly editor of Canadian Metalworking Magazine and has contributed to a wide range of print and on-line publications, including Design Engineering, Canadian Plastics, Service Station and Garage Management, Autovision, and the National Post. He also brings prior industry experience in quality and part design for a Tier One automotive supplier.