CS Events Monthly View

Whether they are exploring the deepest depths of the ocean or
responding to a disaster, robots have proven tremendously effective as
our surrogates, performing tasks that are either too difficult,
dangerous, or dull for humans. The next generation of intelligent
systems will cooperate with people in our homes and workplaces,
providing personalized care, assisting the disabled, and carrying out
advanced manufacturing. To be effective partners, robots must reason
about their environment and their actions in the same way that humans
do. However, today's robots use representations that are either
hard-coded or require significant supervision by a domain expert. I
seek to enable robots to efficiently learn shared cognitive models of
their surroundings and available actions from their interaction with
humans.

This talk highlights my recent advances in semantic perception that
enable robots to acquire shared cognitive models of objects and of
their environment from limited supervision provided by human
partners. First, I will describe a visual appearance-based algorithm
that efficiently learns a robust representation of objects from a
single, user-provided segmentation cue. Second, I will provide a
probabilistic framework that allows robots to formulate human-centric
models of their environment from natural language descriptions. I will
then demonstrate how these learned representations allow people to
command and interact with robots using free-form speech. Finally, I
will end with my vision for how robots will formulate hierarchical
cognitive models of their environments, the objects they contain, and
the rich space of actions available to the robot.

Representative publication: http://people.csail.mit.edu/mwalter/papers/walter13.pdf

Related Video: http://vimeo.com/67438012