|Photo: John Markoff|
This and other activities — including putting a clothes hanger on a rod, inserting a block into a tight space and placing a hammer at the correct angle to remove a nail from a block of wood — may seem like pedestrian actions. But they represent significant advances in robotic learning, by a group of researchers at the University of California, Berkeley, who have trained a two-armed machine to match human dexterity and speed in performing these tasks.
The significance of the work is in the use of a so-called machine-learning approach that links several powerful software techniques that make it possible for the robot to learn new tasks rapidly with a relatively small amount of training.
The new approach includes a powerful artificial intelligence technique known as “deep learning,” which has previously been used to achieve major advances in both computer vision and speech recognition. Now the researchers have found that it can also be used to improve the actions of robots working in the physical world on tasks that require both machine vision and touch.
|Shown, left to right, are Chelsea Finn, Pieter Abbeel, BRETT, Trevor Darrell and Sergey Levine. (Photo courtesy of UC Berkeley Robot Learning Lab)|
The group, led by the roboticist Pieter Abbeel and the computer vision specialist Trevor Darrell, with Sergey Levine, a postdoctoral researcher, and Chelsea Finn, a graduate student, said they were surprised by how well the approach worked compared with previous efforts.
By combining several types of pattern recognition software algorithms known as neural networks, the researchers have been able to train a robot to perfect an action such as correctly inserting a Lego block into another block, with a relatively small number of attempts.
“I would argue this is what has given artificial intelligence the whole new momentum it has right now,” Dr. Abbeel said. “All of a sudden there are all of these results that are better than expected.”
Roboticists said that the value of the Berkeley technology would be in quickly training robots for new tasks and ultimately in developing machines that learn independently.