EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA

Frederick L. Crabbe

2012

Abstract

This paper presents an algorithm that enables a robot to learn from demonstration by inferring a nearly minimal plan instead of the more common policy. The algorithm uses only the demon- strated actions to build the plan, without relying on observation of the world states during the demonstration. By making assumptions about the format of the data, it can generate this plan in O(n5).

Paper Citation

in Harvard Style

L. Crabbe F. (2012). EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSIR, (ICAART 2012) ISBN 978-989-8425-95-9, pages 673-678. DOI: 10.5220/0003881906730678

in Bibtex Style

@conference{ssir12,
author={Frederick L. Crabbe},
title={EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSIR, (ICAART 2012)},
year={2012},
pages={673-678},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003881906730678},
isbn={978-989-8425-95-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSIR, (ICAART 2012)
TI - EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA
SN - 978-989-8425-95-9
AU - L. Crabbe F.
PY - 2012
SP - 673
EP - 678
DO - 10.5220/0003881906730678