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Goldilocks and the Robot Brains

Wednesday, October 20th, 4:10pm Tel Aviv time (3:10pm CET, 9:10am NY time)


Steven M. LaValle, University of Oulu


Imagine building a robot to accomplish one or more tasks, such as
vacuuming, patrolling, or exploration.  This talk considers an
egocentric or situated view of theoretical robot development that
takes into account its space of possible environments and specific
tasks.  How much does a robot need to sense and remember to
successfully interact with its environment?  This question is
fundamental to robotics and distinguishes it from other fields such as
computer science or control theory.  If machine learning is the goal,
then the question becomes what are the minimal, ideal structures
that could possibly be learned?  Thus, emphasis in this talk is placed
on determining the minimal amount of information necessary to solve
tasks, thereby giving the robot the smallest possible "brain".  At one
extreme, strong geometric information is sensed and encoded, leading
to problems such as classical motion planning.  On the path to
minimalism, weak geometric information is considered in the form of
combinatorial or relational sensing and filtering.  Eventually,
topological and set-based representations are considered at the
minimalist extreme.
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