Robot sorts out the tricky stuff
Machine’s speed and accuracy major advance in artificial intelligence
A component-sorting robot picks up items Jan. 16 at the Obeta warehouse in Ludwigsfelde, Germany.
LUDWIGSFELDE, Germany — Inside a warehouse on the outskirts of Berlin, a long line of blue crates moved down a conveyor belt, carrying light switches, sockets and other electrical parts. As they came to a stop, five workers picked through the small items, placing each one in a cardboard box.
At Obeta, an electrical parts company that opened in 1901, it is the kind of monotonous task workers have performed for years.
But several months ago, a new worker joined the team. Stationed behind protective glass, a robot using three suction cups at the end of its long arm does the same job, sifting through parts with surprising speed and accuracy.
While it may not seem like much, this component-sorting robot is a major advance in artificial intelligence and the ability of machines to perform human labor.
“I’ve worked in the logistics industry for more than 16 years and I’ve never seen anything like this,” said Peter Puchwein, vice president of Knapp, an Austrian company that provides automation technology for warehouses.
Standing nearby at the Obeta warehouse, the California engineers who made the robot snapped pictures with their smartphones. They spent more than two years designing the system at a startup called Covariant.AI, building on their research at the University of California, Berkeley.
Their technology is an indication that, in the coming years, few warehouse tasks will be too small or complex for a robot. And as the machines master tasks traditionally handled by humans, their development raises new concerns about warehouse workers losing their jobs to automation.
Because most companies will be slow to adopt the latest robotic technologies, economists believe the advances will not cut into the overall number of logistics jobs anytime soon. But the engineers building these technologies admit that at some point most warehouse tasks will be done by machines.
The engineers at Covariant specialize in a branch of artificial intelligence called reinforcement learning. The machines are wired to learn new tasks on their own through extreme trial and error. And best place to learn is in the real world.
“If you want to advance artificial intelligence, you don’t just do it in a lab,” said Peter Chen, Covariant’s chief executive and co-founder. “There is a huge gap in bringing it to the real world.”
Warehouses are already highly automated. At the facility outside Berlin, other robots have long been used to fetch large boxes from shelves several stories high.
Picking through a bin of random items is different. Shapes vary, as do surfaces. One light switch might be upside down, the other right-side up. The next electrical gadget might be in a plastic bag that reflects light in ways a robot has never seen. A human touch has been needed.
Covariant, which is working with Knapp, built software that could learn through trial and error. First, the system learned from a digital simulation of the task — a virtual re-creation of a bin filled with random items. Then, when Chen and his colleagues transferred this software to a robot, it could pick up items in the real world.
The robot could continue to learn as it sorted through items it had never seen before. Inside the German warehouse, the robot can pick and sort more than 10,000 different items, and it does this with more than 99% accuracy, according to Covariant. the