“Making Robots
Behave”
The fields of AI and
robotics have made great improvements in many individual subfields, including
in motion planning, symbolic planning, probabilistic reasoning, perception, and
learning. Our goal is to develop an
integrated approach to solving very large problems that are hopelessly
intractable to solve optimally. We make
a number of approximations during planning, including serializing subtasks,
factoring distributions, and determinizing stochastic dynamics, but regain
robustness and effectiveness through a continuous state-estimation and
replanning process. I will describe our
initial approach to this problem, as well as recent work on improving
effectiveness and efficiency through learning.
Speaker: Leslie is a Professor at MIT. She has an undergraduate degree in Philosophy
and a PhD in Computer Science from Stanford, and was previously on the faculty
at Brown University. She was the
founding editor-in-chief of the Journal of Machine Learning Research. She is not a robot.
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