
%% \item \textbf{Model aggregation}: Since these observations arrive in a streaming
%%   manner, we split up the data into chunks and use the framework to estimate the
%%   underlying kinematic for each time chunk. We aggregate this information across
%%   all time chunks observed to infer the underlying articulation of the
%%   object. Furthermore, we can provide multiple demonstrations of the same task
%%   and employ the same pipeline to aggregate the articulation information of the
%%   object. 

\section{Model Aggregation}
In the earlier section we mentioned that given the pose observations observed by
the robot, we can estimate the appropriate kinematic structure in a
probabilistic manner. However, we make an assumption on the fact that the 
kinematic structure of the articulated object remains unchanged. More
specifically, we assume that the observed motion profiles the object parts take
are all the degrees of freedom that the object can exercise. In a typical
demonstration, this is not the case. Furthermore, we would like the capability
for robots to learn from multiple demonstrations, and generalize its
understanding of the underlying kinematic model of the articulated object. To
this end, we also propose a model aggregation procedure that accounts for the
variability in motions exercised by the articulated object across longer time
periods, and including multiple demonstrations. 

\textbf{Analysis on Model Estimation in a Fixed Time Range}:
\begin{itemize}
\item Split into even time chunks
\item Evaluate model on each time chunk, and aggregate models across time chunks
\item Aggregation should be able to capture the variability in the model and the
  complexity explainable

\end{itemize}


