CS Colloq: Apprenticeship Learning

Title: Apprenticeship LearningSpeaker: Pieter Abbeel (Stanford)ABSTRACT:
Machine learning is a powerful paradigm which enables autonomous
decision making by learning from examples. Despite its successes,
human learning and decision making still vastly outperform autonomous
decision making, particularly for complex sequential decision making
tasks, where decisions made now have great ramifications far into the
future. In this talk, I will present machine learning techniques with
formal performance guarantees that efficiently learn to perform well
in the apprenticeship learning setting---the setting when expert
demonstrations of the (sequential decision making) task are available.
I will also describe how my apprenticeship learning techniques have
enabled us to solve real-world problems that could not be solved
before. For example, they have enabled a helicopter to perform by far
the most challenging aerobatic maneuvers performed by any autonomous
helicopter to date. They have also enabled us to learn an autonomous
controller for a quadruped robot to traverse challenging terrains and
to learn a variety of different driving behaviours in our highway
driving simulator.BIO:
Pieter Abbeel is a Ph.D. candidate in the Computer Science
Department at Stanford University. His research focuses on machine
learning, including both the foundations of learning, and its practical
application to problems in text mining, computer vision, control,
computational biology, graphics, and computer systems.

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: CS Colloquia