Projects per year
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.
|Title of host publication||Proceedings of the International Cognitive Robotics Workshop (CogRob 2008) at ECAI 2008|
|Number of pages||10|
|Publication status||Published - Jul 2008|
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- 1 Finished
1/02/06 → 31/01/10