\section{Conclusion} 
%
We introduced a framework that enables robots to learn kinematic
models for everyday objects from RGB-D data acquired during
user-provided demonstrations. We combined sparse feature tracking,
motion segmentation, object pose estimation and articulation learning
to learn the underlying kinematic structure of the observed object.
We demonstrated the qualitative and quantitative performance of our
method; it recovers the correct structure more often, and more
accurately, than its predecessor in the literature, and achieves
accuracy similar to that of a marker-based solution.  Our framework
also enables the robot to predict the motion of articulated objects it
has previously learned.  Even given our method's limitation to
recovering open kinematic chains involving only rigid, prismatic or
revolute attachments, its prediction capability may be useful in
future robotic encounters requiring manipulation.

% We show that our framework is able to
% achieve comparable model estimation capabilities to fiducial markers such as
% AprilTags, in spite of functioning in unstructured environments.

% Our prediction results can
% accurately predict the motion manifold on which the articulated object is
% expected to move, given the learned model of the articulated object from
% previous demonstrations. 
