WAREHOUSE ROBOTS LEARNING COMPLEX TASKS
Warehouse robots are wired to learn new tasks through trial and error
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.
As millions of products move through warehouses run by Amazon, Walmart and other retailers, lowwage workers must comb through bin after bin of random stuff — from clothes and shoes to electronic equipment — so that each item can be packaged and sent on its way. Machines had not really been up to the task, until now.
“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.
Because the online retail business is growing so quickly — and 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.
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 the 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, inside a fenced-off room larger than a football field, other robots have long been used to fetch large boxes from shelves several stories high.
But that is a relatively easy task for a machine. Engineers can program a robot to perform the same motion over and over again. The boxes are uniform. A robotcanpickthemupwiththe same motion every time.
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.
Programming a robotic arm to deal with every situation, one rule at a time, is impossible. At Knapp, Puchwein and his partners had tried and failed for years to create a robot with the dexterity and flexibility needed for the job.
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 toarobot,itcouldpickupitems in the real world.
Therobotcouldcontinueto 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 doesthiswithmorethan99% accuracy, according to Covariant. This represents a significant change for the online retail and logistics industries.
Covariant engineers believe their robots will improve with practice. As a robot in one warehouse learns better ways for picking up certain items, the information feeds back to what is essentially a central brain run by Covariant that will help operate machines.
Knapp is considering the design of warehouses staffed by robots rather than humans that would allow for packages to be more densely packed into spaces and retrieved by robots trained to perform the task.
“The new warehouses will be built around AI robots and not humans,” Puchwein said.