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Workshop paper

Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning, R. Petrick, D. Kraft, K. Mourão, C. Geib, N. Pugeault, N. Krüger, and M. Steedman, Proceedings of the International Cognitive Robotics Workshop (CogRob 2008) at ECAI 2008, pages 32-41, 2008.

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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.