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% http://www.springer.com/lncs       Springer Heidelberg 2006/05/04
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\documentclass[runningheads,a4paper]{llncs}

\usepackage{times}
\usepackage{amssymb}
\setcounter{tocdepth}{3}
\usepackage{graphicx}

\usepackage{url}
\urldef{\mailsa}\path|pabbeel@cs.berkeley.edu|
%\urldef{\mailsb}\path|anna.kramer, leonie.kunz, christine.reiss, nicole.sator,|
%\urldef{\mailsc}\path|erika.siebert-cole, peter.strasser, lncs}@springer.com|    
\newcommand{\keywords}[1]{\par\addvspace\baselineskip
\noindent\keywordname\enspace\ignorespaces#1}

\begin{document}

\mainmatter  % start of an individual contribution

% first the title is needed
\title{Machine Learning for Robotics}

% a short form should be given in case it is too long for the running head
\titlerunning{Machine Learning for Robotics}

% the name(s) of the author(s) follow(s) next
%
% NB: Chinese authors should write their first names(s) in front of
% their surnames. This ensures that the names appear correctly in
% the running heads and the author index.
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\author{Pieter Abbeel}
%
\authorrunning{Pieter Abbeel}

% the affiliations are given next; don't give your e-mail address
% unless you accept that it will be published
\institute{University of California, Berkeley, USA\\
\mailsa\\
\url{http://www.cs.berkeley.edu/~pabbeel/}
}

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% NB: a more complex sample for affiliations and the mapping to the
% corresponding authors can be found in the file "llncs.dem"
% (search for the string "\mainmatter" where a contribution starts).
% "llncs.dem" accompanies the document class "llncs.cls".
%

\toctitle{Machine Learning for Robotics}
\tocauthor{Abbeel, Pieter}
\maketitle


\section*{Abstract}

Robots are typically far less capable in autonomous mode than in
tele-operated mode. The few exceptions tend to stem from long days
(and more often weeks, or even years) of expert engineering for a
specific robot and its operating environment. Current control
methodology is quite slow and labor intensive.  I believe advances in
machine learning have the potential to revolutionize robotics.  In
this talk, I will present new machine learning techniques we have
developed that are tailored to robotics. I will describe in depth
``Apprenticeship learning'', a new approach to high-performance robot
control based on learning for control from ensembles of expert human
demonstrations. Our initial work in apprenticeship learning has
enabled the most advanced helicopter aerobatics to-date, including
maneuvers such as chaos, tic-tocs, and auto-rotation landings which
only exceptional expert human pilots can fly. Our most recent work in
apprenticeship learning is providing traction on learning to perform
challenging robotic manipulation tasks, such as knot-tying.  I will
also briefly highlight three other machine learning for robotics
developments: Inverse reinforcement learning and its application to
quadruped locomotion, Safe exploration in reinforcement learning which
enables robots to learn on their own, and Learning for perception with
application to robotic laundry.

\section*{Bio}

Pieter Abbeel received a BS/MS in Electrical Engineering from KU
Leuven (Belgium) and received his Ph.D. degree in Computer Science
from Stanford University in 2008. He joined the faculty at UC Berkeley
in Fall 2008, with an appointment in the Department of Electrical
Engineering and Computer Sciences. He has won various awards,
including best paper awards at ICML and ICRA, the Sloan Fellowship,
the Air Force Office of Scientific Research Young Investigator Program
(AFOSR-YIP) award, the Okawa Foundation award, the 2011's TR35, and
the IEEE Robotics and Automation Society (RAS) Early Career Award. He
has developed apprenticeship learning algorithms which have enabled
advanced helicopter aerobatics, including maneuvers such as tic-tocs,
chaos and auto-rotation, which only exceptional human pilots can
perform. His group has also enabled the first end-to-end completion of
reliably picking up a crumpled laundry article and folding it. His
work has been featured in many popular press outlets, including BBC,
New York Times, MIT Technology Review, Discovery Channel, SmartPlanet
and Wired. His current research focuses on robotics and machine
learning with a particular focus on challenges in personal robotics,
surgical robotics and connectomics.

\end{document}
