Welcome to robot university (only robots need apply)
Want your robot to learn a new task? Then send it to RoboNet, a vast video database that could one day teach it anything.
15 minute de lecture
One of the unsung heroes of the AI revolution is a little-known database called ImageNet. Created by researchers at Princeton University, ImageNet contains some 14 million images, each of them annotated by crowdsourced text that explains what the image shows.
ImageNet is important because it is the database that many of today’s powerful neural networks cut their teeth on. Neural networks learn by looking at the images and accompanying text—and the bigger the database, the better they learn. Without ImageNet and other visual data sets like it, even the most powerful neural networks would be unable to recognize anything.
Now roboticists say they want to try a similar approach with video to teach their charges how to interact with the environment. Sudeep Dasari at the University of California, Berkeley, and colleagues are creating a database called RoboNet, consisting of annotated video data of robots in action. For example, the data might include numerous instances of a robot moving a cup across a table. The idea is that anybody can download this data and use it train a robot’s neural network to move a cup too, even if it has never interacted with a cup before.
Dasari and co hope to build their database into a resource that can pre-train almost any robot to do almost any task—a kind of robot university, which the team calls RoboNet.
Until now, roboticists have had limited success in teaching their charges how to navigate and interact with the environment. Their approach is the standard machine-learning technique that ImageNet helped popularize.
They start by recording the way a robot interacts with, say, a brush to move it across a surface. Then they take many more videos of its motion and use the data to train a neural network on how best to perform the action.
The trick, of course, is to have lots of data—in other words, countless hours of video to learn from. And once a robot has mastered brush-moving, it must go through the same learning procedure to move other almost anything else, be it a spoon or a pair of spectacles. If the environment changes, these learning systems generally have to start all over again.
“The common practice of re-collecting data from scratch for every new environment essentially means re-learning basic knowledge about the world—an unnecessary effort,”say Dasari and co.