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Assistant Professor
Department of Electrical Engineering and Computer Science, UC Berkeley
Berkeley, California USA
http://www.cs.berkeley.edu/~pabbeel
Title: Machine Learning and Optimization for Robotics
Slides
Abstract:
In this talk I will describe two main ideas. First, I will describe
apprenticeship learning, a new approach to equip robots with skills
through learning from ensembles of expert human demonstrations. Our
initial work in apprenticeship learning 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. In our current work we are studying how a robot
could learn to perform challenging robotic manipulation tasks, such as
knot-tying. Second, I will describe inverse optimal control, which
considers the problem of observing a (potentially noisy) optimal
controller and recovering the underlying cost function that is being
optimized. This is important in human robot interaction, to enable
robots to gauge their collaborators’ objectives, as well as in
teaching robots how to perform complicated tasks. Third, I will
describe advances in belief space planning, where, rather than
planning in the original state space, we plan in the space of
probability distributions over states. Optimal plans in belief space
do not only plan for actions that affect the state of the system, but
also for information gathering actions, which can be essential in the
presence of significant uncertainty. While in general such problems
are intractable, I will present approximate solutions obtained through
Gaussian belief space planning that perform well in practice.
Biography:
Pieter Abbeel received his Ph.D. degree in Computer Science from
Stanford University in 2008 and is currently on the faculty at UC
Berkeley 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
Office of Naval Research Young Investigator Program (ONR-YIP) award,
the DARPA Young Faculty Award (DARPA-YFA), the Okawa Foundation award,
the TR35, the IEEE Robotics and Automation Society (RAS) Early Career
Award, and the Dick Volz Best U.S. Ph.D. Thesis in Robotics and
Automation 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 emphasis on
challenges in personal robotics and surgical robotics.
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