\section{Experiments and Analysis}

% As stated earlier, our framework focuses on tracking and learning the kinematic
% model of an object in unstructured environments, given multiple user-provided
% demonstrations of the object being manipulated. This allows the robot 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. We show strong qualitative performance, as seen in
% Figure~\ref{fig:results-fig}, in learning and predicting the kinematic models of
% articulated objects commonly found in the household environment.

% In the following section, we benchmark the performance of our framework using a
% data set we collected consisting of several visual demonstrations of articulated
% objects commonly found in the household environment. 

Our experimental setup consists of a single sensor providing RGB-D
depth imagery. Each visual demonstration involved a human manipulating
an articulated object and its parts at a normal pace, while avoiding
obscuration of the object from the robot's perspective.
Demonstrations were performed for multiple robot viewpoints, to
capture variability in depth imagery. We performed 41 demonstration
sessions by manipulating a variety of household objects:
refrigerators, doors, drawers, laptops etc. Each demonstration was
recorded for about 30-60 seconds. April tags~\cite{olson2011tags} were
used to recover ground truth estimates of each articulated object's
motion, which we adopted as a baseline for evaluation. In order to
avoid any influence on our method of observations arising from
fiducial markers, the RGB-D input was pre-processed to mask out
regions containing the tags.

We then compared the pose estimation, model selection and estimation
performance of our method to that of an alternative state-of-the-art
method (reimplemented by us based on~\cite{katz2013interactive}), and
to traditional methods using fiducial markers.  We incorporated
several improvements~\cite{kalal2010forward},\cite{paris2006fast} to
Katz's algorithm, as previously described in
Section~\ref{sec:feature-tracking}, 
to enable fair comparison with our proposed method.

\subsubsection{Qualitative and Overall Performance}
Figure~\ref{fig:results-fig} shows the method in operation for
household objects including a laptop, a microwave, a refrigerator and
a drawer. Tables~\ref{tab:pose-estimation-comparison}
and~\ref{tab:model-estimation-comparison} compare the performance of
our method in estimating the kinematic model parameters for several
articulated objects observed from a variety of viewpoints.  Our method
recovered a correct model for more objects, and for almost every
object tested recovered model parameters more accurately, than Katz's
method.

\input{sec-analysis-rgbd-data}
