The system is designed to address the fact that while robots in controlled environments like assembly lines are able to pick up the same object over and over again, the systems don't truly understand objects' shapes. While advances in computer vision have enabled robots to make basic distinctions between objects, there's still "little the robots can do after a quick pick-up."
The new system lets robots inspect random objects, and visually understand them enough to accomplish specific tasks without ever having seen them before. The system, called Dense Object Nets (DON), looks at objects as collections of points that serve as sort of visual roadmaps.
This approach, say the researchers, lets robots better understand and manipulate items, and, most importantly, allows them to even pick up a specific object among a clutter of similar objects. For example, the system might be used to get a robot to grab onto a specific spot on an object, such as the tongue of a shoe. In that case, it can look at a shoe it has never seen before, and successfully grab its tongue.
"Many approaches to manipulation can't identify specific parts of an object across the many orientations that object may encounter," says PhD student Lucas Manuelli, co-author of a paper about the system. "For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side."
Two common approaches to robot grasping involve either task-specific learning, or creating a general grasping algorithm. Such techniques have their drawbacks: Task-specific methods are difficult to generalize to other tasks, and general grasping doesn't get specific enough to deal with the nuances of particular tasks, like putting objects in specific spots.
However, the DON system, say the researchers, essentially creates a series of coordinates on a given object, which serve as a kind of visual roadmap, to give the