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<div align="center"><font size=4><b>The UCSD Department of Cognitive
Science is pleased to announce a talk by<br><br>
</font><font size=6>Pieter Abbeel<br><br>
</font><font size=4>Department of Computer Science<br>
Stanford University<br><br>
</font><font face="arial" size=4>Friday, February 29, 2008 at 12pm<br>
Cognitive Science Building, room 003<br><br>
<br>
</font><font size=5>"Apprenticeship Learning for Robotic Control
with Application to Quadruped Locomotion and Autonomous Helicopter
Flight."<br><br>
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Many problems in robotics have unknown, stochastic, high-dimensional, and
highly non-linear dynamics, and offer significant challenges to classical
control methods. Some of the key difficulties in these problems are that
(i) It is often hard to write down, in closed form, a formal
specification of the control task (for example, what is the objective
function for "flying well"?), (ii) It is difficult to build a
good dynamics model because of both data collection and data modeling
challenges (similar to the "exploration problem" in
reinforcement learning), and (iii) It is expensive to find closed-loop
controllers for high dimensional, highly stochastic domains. In this
talk, I will present learning algorithms with formal performance
guarantees which show that these problems can be efficiently addressed in
the apprenticeship learning setting---the setting when expert
demonstrations of the task are available. I will also present how my
apprenticeship learning techniques have enabled us to solve real-world
control problems that could not be solved before: They have enabled a
quadruped robot to traverse challenging terrain, and a helicopter to
perform by far the most challenging aerobatic maneuvers performed by any
autonomous helicopter to date, including maneuvers such as chaos and
tic-tocs, which only exceptional expert human pilots can fly.</b></body>
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