Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Principled Methods for Behavior-Based Control and Learning Applied to
Robot Teams and Humanoids
Maja J Mataric'
University of Southern California
Wednesday, May 24, 2000
refreshments 4:05PM
talk begins 4:15PM
TCseq201, Lecture Hall B
http://robotics.stanford.edu/ba-colloquium/
Abstract
Behavior-based control, which exploits the dynamics of collections of
concurrent, interacting processes coupled to the external world, is
both biologically relevant and effective for control problems
featuring local information, uncertainty, and non-stationarity. In
this talk we describe methods we have developed for principled
behavior-based control and learning in two problem domains:
multi-robot coordination and humanoid imitation.
In the multi-robot domain the key challenges involve reconciling
individual and group-level goals and achieving scalable, on-line
real-time learning. How to do all of this in a distributed
behavior-based way in a timely and consistent fashion? We describe
methods for Pareto-optimal behavior selection for principled group
coordination, publish/subscribe messaging for distributed
communication, and augmented Markov models for on-line real-time model
building for group adaptation. The results are demonstrated on groups
of locally-controlled but globally efficient mobile robots performing
distributed collection, multiple-target-tracking, and object
manipulation. In the second part of the talk we describe the
application of behavior-based control in the form of basis behaviors
or primitives to the problem of humanoid control. Here, the
challenges include the high dimensionality of the system and the need
for tight coupling between the perceptual and motor systems. We
describe an imitation model that employs direct sensory-motor mappings
within the behavior-based framework to segment and map the observed
movement onto the existing motor system. The same biologically
motivated method facilitates recognition, classification, prediction,
and learning. The results are demonstrated on a 20 degree-of-freedom
dynamic humanoid imitating dance and sports movements from visual data
of human demonstrations.
About the Speaker
Maja Mataric is an assistant professor in the Computer Science
Department and the Neuroscience Program at the University of Southern
California, Director of the USC Robotics Research Labs and Associate
Director of the Institute for Robotics and Intelligent Systems. She
received her PhD and MS degrees in Computer Science and AI from MIT in
1994, and 1990, respectively. She is a recipient of the NSF Career
Award, the MIT TR100 Innovation Award, the IEEE Robotics and
Automation Society Early Career Award, and is featured in the upcoming
movie about scientists, "Me & Isaac Newton". In her collaborations,
she has interacted with a variety of human and robotic colleagues
(ranging from LEGO robots to humanoids) at NASA's Jet Propulsion Lab,
the Free University of Brussels AI Lab, LEGO Cambridge Research Labs,
GTE Research Labs, the Swedish Institute of Computer Science, and ATR
Human Information Processing Labs in Japan. Her Interaction Lab at
USC performs research in the areas of control and learning in
behavior-based multi-robot systems and humanoids.
bac-coordinators@
cs.stanford.edu
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Last modified: Mon May 1 11:58:48 2000