Many functional elements of human homes and workplaces consist of
rigid components which are connected through one or more sliding
or rotating linkages.  Examples include doors and drawers of
cabinets and appliances; laptops; and swivel office chairs.  A robotic
mobile manipulator would benefit from the ability to acquire kinematic
models of such objects from observation. This paper describes a method
by which a robot can acquire an object model by capturing depth
imagery of the object as a human moves it through its range of motion.
We envision that in future, a machine newly introduced to an
environment could be shown by its human user the articulated
objects particular to that environment, inferring from these
``visual demonstrations'' enough information to actuate each
object independently of the user.

Our method employs sparse (markerless) feature tracking, motion
segmentation, component pose estimation, and articulation learning; it
does not require prior object models.  Using the method, a robot can
observe an object being exercised, infer a kinematic model
incorporating rigid, prismatic and revolute joints, then use the model
to predict the object's motion from a novel vantage point.  We
evaluate the method's performance, and compare it to that of a
previously published technique, for a variety of household objects.
