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

Ronald Petrick, Dirk Kraft, Kira Mourao, Christopher Geib, Nicolas Pugeault, Norbert Krüger, Mark Steedman

Research output: Contribution to conference without publisher/journalPaperResearchpeer-review

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, knowledgelevel 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 lepresentation from low-level world state information.
Original languageEnglish
Publication date2008
Publication statusPublished - 2008
EventThe 6th International Cognitive Robotics Workshop (CogRob 2008) - Patras, Greece
Duration: 21. Jul 200822. Jul 2008
Conference number: 6

Conference

ConferenceThe 6th International Cognitive Robotics Workshop (CogRob 2008)
Number6
Country/TerritoryGreece
CityPatras
Period21/07/200822/07/2008

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