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. 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.

“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 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.

Warehouses are already highly automated. But 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.

The engineers at Covariant specialize in a branch of artificial intelligence called reinforcement learning. 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 Peter Chen, Covariant’s chief executive and co-founder, 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. According to Covariant, 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. 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. This represents a significant change for the online retail and logistics industries.

Late last year, international robot maker ABB ran a contest. It invited 20 companies to design software for its robot arms that could sort through bins of random items, from cubes to plastic bags filled with other objects. Most came nowhere close to passing the test. Covariant was the only company that could handle every task as swiftly and efficiently as a human.

“We were trying to find weaknesses,” said Marc Segura, managing director of service robotics at ABB. “It is easy to reach a certain level on these tests, but it is super difficult not to show any weaknesses.”

Dirk Jandura, the managing director of Obeta, said companies like his were under extreme pressure to be more efficient. Automation is a key way to keep costs low. Knapp, which helped deploy the system outside Berlin, and ABB believe this technology can be used in similar warehouses.

Knapp plans to make it hard for companies to say no to replacing human workers with robots. Puchwein said they would charge a fee that was always lower than what a company would pay a human.

Beth Gutelius, associate director of the Center for Urban Economic Development at the University of Illinois at Chicago, who has studied the impact of automation on work, said this kind of technology was unlikely to shift the job market any time soon. The greater problem, she said, is that as humans work alongside robots, they will be judged in new ways. “As we start to compare the speed and efficiency of humans to robots, there is a whole new set of health and safety issues that emerge,” she said.

Pieter Abbeel, a Berkeley professor who is a co-founder of Covariant as well as its president and chief scientist, said humans would continue to work alongside machines in these kinds of warehouses. But he acknowledged that the job market would significantly shift as machine learning improved. “If this happens 50 years from now, there is plenty of time for the educational system to catch up to the job market,” he said.

Source: Toronto Star