The team at the MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), developed a novel system which lets robots inspect random objects, and visually understand them enough to accomplish specific tasks without ever having seen them before.
This approach lets robots better understand and manipulate items and allows them to even pick up a specific object among a clutter of similar objects -- a valuable skill for the kinds of machines that companies like Amazon and Walmart use in their warehouses, the researchers said.
The DON system essentially creates a series of coordinates on a given object, which serves as a kind of "visual roadmap" of the objects, to give the robot a better understanding of what it needs to grasp, and where.
It is "self-supervised" and does not require any human annotations.
In the study, one set of tests done on a soft caterpillar toy, a Kuka robotic arm powered by DON could grasp the toy's right ear from a range of different configurations.
This showed that, among other things, the system has the ability to distinguish left from right on symmetrical objects.
"In factories robots often need complex part feeders to work reliably," Manuelli said.
The team will present their paper on the system at the forthcoming Conference on Robot Learning in Zurich, Switzerland.
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