A relational approach to tool-use learning in robots

Download files
Access & Terms of Use
open access
Copyright: Brown, Solly Ashley
Altmetric
Abstract
This thesis presents a robot agent which learns to exploit objects in its environment as tools, allowing it to solve problems which would otherwise be impossible to achieve. Our agent learns by watching a single demonstration of tool use by a teacher, and then by experimenting in the world with a variety of available tools. The emphasis in our approach is on learning tool-use in a relational context, and our agent is able to generalise across objects and tasks to learn the spatial and structural constraints which describe useful tools and how they should be employed. Two learning mechanisms are employed to achieve this: learning by explanation, and learning by trial-and-error. A form of explanation-based learning is used to identify the most important sub-goals the teacher was able to achieve by using the tool. The action model constructed via this explanation is then refined through trial-and-error experimentation and the use of a novel Inductive Logic Programming (ILP) algorithm.
Persistent link to this record
Link to Publisher Version
Link to Open Access Version
Additional Link
Author(s)
Brown, Solly Ashley
Supervisor(s)
Sammut, Claude
Creator(s)
Editor(s)
Translator(s)
Curator(s)
Designer(s)
Arranger(s)
Composer(s)
Recordist(s)
Conference Proceedings Editor(s)
Other Contributor(s)
Corporate/Industry Contributor(s)
Publication Year
2009
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
Files
download whole.pdf 1.26 MB Adobe Portable Document Format
Related dataset(s)