A robot has worked out how to pick up awkward and unusual objects by working with virtual objects.
A team from the University of California, Berkeley, fed images to a large deep-learning neural network connected to an off-the-shelf 3-D sensor and a standard robot arm. When a new object is placed in front of it, the robot’s deep-learning system quickly figures out what grasp the arm should use.
In tests, when the robot was more than 50 percent confident it could grasp an object, it succeeded in lifting the item and shaking it without dropping it 98 percent of the time. When it was unsure, it would poke the object in order to figure out a better grasp. After doing that it was successful at lifting it 99 percent of the time.
Ken Goldberg, a Berkeley professor who led the work, said many researchers are working on ways for robots to learn to grasp and manipulate things by practicing over and over, but the process is very time-consuming. The new robot learns without needing to practice, and he says it is significantly better than any previous system.
“We’re producing better results but without that kind of experimentation,” he said. “We’re very excited about this.”
Goldberg and colleagues plan to release the data set they created. Public data sets have been important for advancing the state of the art in computer vision, and now new 3-D data sets promise to help robots advance.
The emergence of more dexterous robots could have significant economic implications, too. The robots found in factories can be very precise and determined, but also clumsy when faced with an unfamiliar object. Companies such as Amazon are using robots in warehouses but so far only for moving products around, not for picking objects for orders.
The Berkeley researchers also collaborated with Juan Aparicio, a research group head at Siemens, who is interested in commercialising cloud robotics, among other connected manufacturing technologies.
Aparicio says the research is exciting because the reliability of the arm offers a clear path toward commercialization.
Developments in machine dexterity may also be significant for the advancement of artificial intelligence. Manual dexterity played a critical role in the evolution of human intelligence, forming a virtuous feedback loop with sharper vision and increasing brain power. The ability to manipulate real objects more effectively seems certain to play a role in the evolution of artificial intelligence, too.
[University of California, Berkeley, professor Ken Goldberg (left) and Siemens Research Group head, Juan Aparicio. Photo: UCB]