Smart Robots: Out of the Lab, Into the World

For being one of the world’s leading artificial intelligence (AI) roboticists, Professor Pieter Abbeel doesn’t watch much sci-fi. Pieter is the Director of the UC Berkeley Robot Learning Lab and co-director of the Berkeley Artificial Intelligence (BAIR) Lab. In a recent podcast with host Abigail Hing Wen, Pieter said he first became enamored with robots not from popular depictions in the media, but when he thought, “What if I write a piece of code and it beats me in chess? How can I write a piece of code that beats me, even though I wrote the piece of code?”

Though many people have since developed chess-playing robots that can beat most humans, Pieter included, that thought inspired him to eventually research and build ever more intelligent systems, setting up a research group at Berkeley to advance reinforcement learning, hoping eventually to found a robotics company. Today, while he continues to work at Berkeley, he is also a founder of Covariant, a company that’s seeking to build robots that can adapt to what they see and even learn from their own experience to solve problems.

Robots in Warehousing

In the podcast, Pieter says he and his Covariant colleagues met with nearly two hundred companies and concluded that warehousing and manufacturing are two fields where it's most natural to start introducing learning robots. The key is that this environment is too challenging for traditional robots: these blindly repeat the same motion over and over, relying on very high precision components for accuracy, rather than a visual feedback loop. (I actually got my own start in machine vision working on a camera-based system to automatically slow or stop robots when approached by factory workers, which might otherwise weld them to a passing chassis.) While too variable for traditional robots, these are still quite a controlled environment relative to, say, the floor of my kid’s playroom, hitting a difficulty sweet spot for Covariant’s machines. Crucially, Covariant is able to target tasks with a low cost of failure, enabling their robots to learn from experience, including mistakes, in a way that they couldn’t in an automotive or aviation context.

Covariant is moving into a fulfillment niche that has seen huge expansion driven by online ordering. Over the last ten years, the fulfillment industry has shifted from having humans fetching things from endless shelves to what is called a “goods-to-person” system, where an entire automated 3D grid system retrieves several items together in a tote—feasible with traditional robots because totes are standard shapes, mounted on standardized racks. But for robots, this is also where a challenge begins: how can they decipher which items in the tote need to be separated and shipped to different addresses? For humans it’s very easy: the order of a video game controller goes to one address, the order for a phone charger goes to a different address. It's hard for the robot to do this kind of sorting because larger warehouses might contain over a million different items in storage.