Browsing by Author "Mason, Julian"

The world is full of objects: cups, phones, computers, books, and

countless other things. For many tasks, robots need to understand that

this object is a stapler, that object is a textbook, and this other

object is a gallon of milk. The classic approach to this problem is

object recognition, which classifies each observation into one of

several previously-defined classes. While modern object recognition

algorithms perform well, they require extensive supervised training:

in a standard benchmark, the training data average more than four

hundred images of each object class.

The cost of manually labeling the training data prohibits these

techniques from scaling to general environments. Homes and workplaces

can contain hundreds of unique objects, and the objects in one

environment may not appear in another.

We propose a different approach: object discovery. Rather than rely on

manual labeling, we describe unsupervised algorithms that leverage the

unique capabilities of a mobile robot to discover the objects (and

classes of objects) in an environment. Because our algorithms are

unsupervised, they scale gracefully to large, general environments

over long periods of time. To validate our results, we collected 67

robotic runs through a large office environment. This dataset, which

we have made available to the community, is the largest of its kind.

At each step, we treat the problem as one of robotics, not disembodied

computer vision. The scale and quality of our results demonstrate the

merit of this perspective, and prove the practicality of long-term

large-scale object discovery.