The world’s most dexterous robot can recognize and handle complex shapes and sort materials with lightning efficiency. The low-key looking robot has been designed by Ken Goldberg a U.C. Berkeley professor and his graduate student Jeff Mahler.

The key to the nimble robot is the use of software called Dex-Net that allows the robot to pick up even odd shaped objects with speed. Professor Goldberg showed off the latest version of the robot in San Francisco at EmTech Digital, an event dedicated artificial intelligence hosted by MIT Technology Review.

The unassuming robot dexterity would have useful applications in hospitals and warehouses. Dex-Net learns to grasp by trying to pick up objects in a virtual environment.

Dex-Net trained for hours in simulation

This process trains a deep neural network through trial and error. While this method of learning is lengthy, it reaches an outcome where Dex-Net can recognize an object it has never seen before. It can even learn to tune an object over to examine it more carefully before deciding on the best way to grasp it.

New metric helps comparison

As each object is presented to the robot, it is scanned and then the software looks at both networks before making a quick decision whether to suck or grip the object in question. The world’s most dexterous robot got its title from a newly designed way to measure the robot picking success.

The best robots out there are currently capable of roughly 70 and 95 mean picks per hour. Humans capable of between 400 and 600 and Goldberg's new Dex-Net machine can achieve a pretty impressive 200 to 300 mean picks per hour.