Covariant Uses Simple Robot and Gigantic Neural Net to Automate Warehouse Picking

Evan Ackerman, Covariant Uses Simple Robot and Gigantic Neural Net to Automate Warehouse, IEEE Spectrum, 29 Jan 2020.

There’s already a huge amount of automation in logistics, but as Abbeel explains, in warehouses there are two separate categories that need automation: “The things that people do with their legs and the things that people do with their hands.” The leg automation has largely been taken care of over the last five or 10 years through a mixture of conveyor systems, mobile retrieval systems, Kiva-like mobile shelving, and other mobile robots. “The pressure now is on the hand part,” Abbeel says. “It’s about how to be more efficient with things that are done in warehouses with human hands.”

A huge chunk of human-hand tasks in warehouses comes down to picking. That is, taking products out of one box and putting them into another box. In the logistics industry, the boxes are usually called totes, and each individual kind of product is referred to by its stock keeping unit number, or SKU. Big warehouses can have anywhere from thousands to millions of SKUs, which poses an enormous challenge to automated systems. As a result, most existing automated picking systems in warehouses are fairly limited. Either they’re specifically designed to pick a particular class of things, or they have to be trained to recognize more or less every individual thing you want them to pick. Obviously, in warehouses with millions of different SKUs, traditional methods of recognizing or modeling specific objects is not only impractical in the short term, but would also be virtually impossible to scale. 

This is why humans are still used in picking—we have the ability to generalize. We can look at an object and understand how to pick it up because we have a lifetime of experience with object recognition and manipulation. We’re incredibly good at it, and robots aren’t. “From the very beginning, our vision was to ultimately work on very general robotic manipulation tasks,” says Abbeel. “The way automation’s going to expand is going to be robots that are capable of seeing what’s around them, adapting to what’s around them, and learning things on the fly.” 

Covariant is tackling this with relatively simple hardware, including an off-the-shelf industrial arm (which can be just about any arm), a suction gripper (more on that later), and a straightforward 2D camera system that doesn’t rely on lasers or pattern projection or anything like that. What couples the vision system to the suction gripper is one single (and very, very large) neural network, which is what helps Covariant to be cost effective for customers. “We can’t have specialized networks,” says Abbeel. “It has to be a single network able to handle any kind of SKU, any kind of picking station. In terms of being able to understand what’s happening and what’s the right thing to do, that’s all unified. We call it Covariant Brain, and it’s obviously not a human brain, but it’s the same notion that a single neural network can do it all.” 

Adam Satariano and Cade Metz, A Warehouse Robot Learns to Sort Out the Tricky Stuff, NYT,  Jan. 29, 2020.

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.”... 

Late last year, the 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. 

Ten of the companies were based in Europe, and the other half were in the United States. Most came nowhere close to passing the test. A few could handle most tasks but failed on the trickier cases. Covariant was the only company that could handle every task as swiftly and efficiently as a human.... 

Like many warehouse operators, Obeta has trouble finding workers willing to do the monotonous work. Each picker handles about 170 orders an hour, or about three per minute, over an eight-hour day. In the summer, temperatures in the warehouse reach more than 100 degrees. It is hard to keep employees for longer than six months.