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Fall 2010 GRASP Seminar – Drew Bagnell, Carnegie Mellon University, “Imitation Learning, Inverse Optimal Control and Purposeful Prediction”

October 22, 2010 @ 11:00 am - 12:00 pm

Programming robots is hard. While demonstrating a desired behavior may
be easy, designing a system that behaves this way is often difficult,
time consuming, and ultimately expensive. Machine learning promises to
enable “programming by demonstration” for developing high-performance
robotic systems. Unfortunately, many approaches that utilize the
classical tools of supervised learning fail to meet the needs of
imitation learning.

Perhaps foremost, classical statistics and supervised machine learning
exist in a vacuum: predictions made by these algorithms are explicitly
assumed to not affect the world in which they operate.
I’ll discuss the problems that result from ignoring the effect of
actions influencing the world, and I’ll highlight simple “reduction-
based” approaches that, both in theory and in practice, mitigate these

Additionally, robotic systems are often built atop sophisticated
planning algorithms that efficiently reason far into the future;
consequently, ignoring these planning algorithms in lieu of a
supervised learning approach often leads to poor and myopic
performance. While planners have demonstrated dramatic success in
applications ranging from legged locomotion to outdoor unstructured
navigation, such algorithms rely on fully specified cost functions
that map sensor readings and environment models to a scalar cost. Such
cost functions are usually manually designed and programmed. Recently,
our group has developed a set of techniques that learn these functions
from human demonstration by applying an Inverse Optimal Control (IOC)
approach to find a cost function for which planned behavior mimics an
expert’s demonstration. These approaches shed new light on the
intimate connections between probabilistic inference and optimal

I’ll consider case studies in activity forecasting of drivers and
pedestrians as well as the imitation learning of robotic locomotion
and rough-terrain navigation. These case-studies highlight key
challenges in applying the algorithms in practical settings.


- Learn More

J. Andrew (Drew)
Bagnell is an Associate Professor in the Robotics Institute and Machine
Learning Departments at Carnegie Mellon University. He received his PhD
from Carnegie Mellon in 2004. Bagnell’s research focuses on the
intersection of machine learning with computer vision, optimal
control, and robotics. His interests in machine learning range from
algorithmic and theoretical development to delivering fielded
learning-based systems.


October 22, 2010
11:00 am - 12:00 pm
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