\section{Introduction}
% As robots have become increasingly able
% to mitigate uncertainty, there is growing demand for robots that can assist
% people in our home as well as health care, manufacturing, and logistics
% settings. A long-standing challenge is to develop machines that can interact
% effectively with the diverse, complex objects built by humans for their own
% use. Existing approaches to manipulation often assume either a limited set of
% objects for which the interactions can be pre-defined or, provide a means of
% learning available interactions for a small number of simple objects, typically
% after extensive training. These methods are not well-suited to the articulated
% objects commonly found in man-made environments (e.g., drawers, doors,
% refrigerators, chairs, etc.), whose models can be complex.

A long-standing challenge in robotics is to endow robots with the
ability to interact effectively with the diversity of objects common
in human-made environments. Existing approaches to manipulation often
assume that objects are simple and drawn from a small set. The models
are then either pre-defined or learned from training, for example
requiring fiducial markers on object parts, or prior assumptions
about object structure.  Such requirements may not scale well as
the number and variety of objects increases.  This paper describes
a method with which robots can learn kinematic models for articulated 
objects in situ, simply by observing a user manipulate the object.
Our method learns open kinematic chains that involve rigid
attachments, and prismatic and revolute motions, between parts.

%whose models can be complex.
%We argue that autonomous
%manipulation can be further facilitated with specific knowledge of the underlying kinematic
%model of the articulated object being manipulated. 

% Intro figure
%
\begin{figure}[!t]
  \centering
  \begin{tabular}{c}
    \includegraphics[width=\columnwidth]{graphics/intro-fig/intro-fig.png}\\
  \end{tabular}
  \caption{The proposed framework reliably learns the
    underlying kinematic model of multiple articulated objects from
    user-provided visual demonstrations, and subsequently predicts their motions
    at future encounters.}
  \label{fig:intro-fig}
\end{figure}

% In order to opportunistically learn articulation models in unstructured
% environments, we need the ability to simultaneously track and learn previously
% unseen objects in the scene robustly. Existing methods expect the robot to
% operate in structured environments with prior models of the objects being
% manipulated. Furthermore, fiducial tags are often used to provide rich
% noise-free data as observations to the robot.  We
% designed our framework with the specific need to work in unstructured
% environments, and provide the robot with the capability to opportunistically
% learn new models of articulated objects, without having to manually set up rich
% fiducial information in the scene prior to the visual demonstration.

There are three primary contributions of our approach that make it
effective for articulation learning. First, we propose a feature
tracking algorithm designed to perceive articulated motions in
nunstructured environments, avoiding the need to embed fiducial markers
in the scene.  Second, we describe a kernel-based motion segmentation
algorithm that uses clustering to group feature trajectories arising
from each object part. A subsequent optimization step recovers the
6-DOF pose of each object part. Third, the method enables use of the
learned articulation model to predict the object's motion when it is
observed from a novel vantage point. Figure~\ref{fig:intro-fig}
illustrates a scenario where our method learns kinematic models for a
refrigerator and microwave from separate user-provided demonstrations,
then predicts the motion of each object in a subsequent encounter.  We
present experimental results that demonstrate the use of our method to
learn kinematic models for a variety of everyday objects, and compare
our method's performance to that of the current state of the art.
