Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning

Ron Petrick, Dirk Kraft, Kira Mourao, Christopher Geib, Nico Pugeault, Norbert Krüger, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe an approach to integrated robot control, high-level planning, and action effect learning that attempts to overcome the representational difficulties that exist between these diverse areas. Our approach combines ideas from robot vision, knowledge-level planning, and connectionist machine learning, and focuses on the representational needs of these components. We also make use of a simple representational unit called an instantiated state transition fragment (ISTF) and a related structure called an object-action complex (OAC). The goal of this work is a general approach for inducing high-level action specifications, suitable for planning, from a robot’s interactions with the world. We present a detailed overview of our approach and show how it supports the learning of certain aspects of a high-level representation from low-level world state information.
Original languageEnglish
Title of host publicationProceedings of the International Cognitive Robotics Workshop (CogRob 2008) at ECAI 2008
Pages32-41
Number of pages10
Publication statusPublished - Jul 2008

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