This robot learns to pick up mugs by first learning a theory of mugness
For all of the recent progress in machine intelligence, robots still struggle to adapt relatively simple tasks to new situations. Take, for example, picking up a mug and hanging it on a mug rack; even small changes in a mug’s shape, size, color, and orientation can throw a robot off.
In a new paper, researchers at MIT are now proposing a new technique for helping robots generalize their learning with relatively little data. They do so by training a neural network to extract just a few key points from a mug or other object that needs to be picked up and placed, giving the robot a visual road map for how to grasp and orient it. During testing, they found that the bot only needed three key points for a mug—one on the center of its side, one on the bottom, and one on the handle—and six key points for a shoe.
Unlike previous techniques that require hundreds or even thousands of examples for a robot to learn to pick up a mug it has never seen before, this approach requires only a few dozen. The researchers were able to train the neural network on 60 scenes of mugs and 60 scenes of shoes to reach a similar level of performance. When the system initially failed to pick up high heels because there were none in the data set, they quickly fixed the problem by adding a few labeled scenes of high heels to the data.
The team hopes to use the approach to tackle bigger tasks next, like unloading a dishwasher or wiping down a kitchen counter.
This story originally appeared in our AI newsletter The Algorithm. To have it directly delivered to your inbox, sign up here for free.
Deep Dive
Artificial intelligence
Large language models can do jaw-dropping things. But nobody knows exactly why.
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.
Google DeepMind’s new generative model makes Super Mario–like games from scratch
Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.
What’s next for generative video
OpenAI's Sora has raised the bar for AI moviemaking. Here are four things to bear in mind as we wrap our heads around what's coming.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.