[robotics-worldwide] RSS Workshop on Bridging the Gap between high-level representation and low-level behaviors - Deadline Extended to April 23

Dana Kulic dana at ynl.t.u-tokyo.ac.jp
Mon Apr 20 06:02:57 PDT 2009


RSS 2009 Workshop on bridging the gap between high-level discrete
representations and low-level continuous behaviors

Official Workshop of the Technical Committee on Robot Learning


Sunday, June 28, 2009 in Seattle, WA
As part of the Robotics: Science and Systems Conference

Important Dates

April 23, 2009  Poster Abstract Submission Deadline (NOTE: New
Extended Deadline)
May 15, 2009  Notification of Acceptance
June 28, 2009  Workshop


Recently, robotics researchers have been investigating the modeling of
human and robot behavior in terms of motion primitives. This research
direction, based on biological and neuroscience findings, posits that
human behavior is composed of motor primitive units, which can be
acquired by a robot through imitation learning or practice. Motion
primitives offer an approach for discretizing continuous behavior,
representing a "bottom-up" approach for organizing robot behavior.  On
the other hand, in AI and planning fields, there has been a
longstanding area of research in planning and acting in the discrete
domain, or through modeling changes in the world as an instantaneous
change in discrete state. This approach can be thought of as a
"top-down" approach for organizing robot behavior. In this workshop,
we hope to bring together researchers from both areas to discuss
approaches for "bridging the gap" and combining continuous domain
approaches with discrete representations.

Topics of Interest

We invite poster submissions from researchers working on combining
discrete representations with continuous behaviors for robotic
systems.  Specific topics of interest include:

-          motion primitive representations and task abstractions
-          learning and parsing sequences and plans of motion primitives
-          imitation learning and learning from observation based on
motion primitives
-          hierarchical reinforcement learning
-          apprenticeship learning of composed tasks
-          hybrid task control
-          hierarchical organization of behaviors
-          learning operator conditions for primitives
-          plan recognition
-          plan generation and modification

Submission Guidelines

Authors should submit a 1-2 page abstract of their poster by email to
postersubmissions at learning-robots.de, by April 20, 2009.

Workshop Organizers
Dana Kulić
Department of Mechano-Informatics
University of Tokyo
dana at learning-robots.de

Pieter Abbeel
Department of Electrical Engineering and Computer Science
University of California at Berkeley
pabbeel at cs.berkeley.edu

Jan Peters
Department for Empirical Inference and Machine Learning
Max Planck Institute of Biological Cybernetics
mail at jan-peters.net

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