  Constrained objects, such as doors and drawers are often complex and share a
similar structure in the human environment. A robot needs to interact
accurately with constrained objects to safely and successfully complete a task.
Learning from Demonstration offers an appropriate path to learn the object
structure of the demonstration for unknown objects for unknown tasks. There is
work that extracts the kinematic model from motion. However, the gap remains
when the robot faces a new object with a similar model but different contexts,
e.g. size, appearance, etc. In this paper, we propose a framework that
integrates all the information needed to learn a constrained motion from a
depth camera into a descriptor of the constrained task. The descriptor consists
of object information, grasping point model, constrained model, and reference
frame model. By associating constrained learning and reference frame with the
constrained object, we demonstrate that the robot can learn the book opening
model and parameter of the constraints from demonstration and generalize to
novel books.
