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Title: A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams

Abstract

While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal ofmore » this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality.« less

Authors:
Publication Date:
Research Org.:
Oak Ridge National Lab., TN (US)
Sponsoring Org.:
USDOE Office of Science (US)
OSTI Identifier:
14971
Report Number(s):
ORNL/CP-105499
TRN: AH200130%%152
DOE Contract Number:  
AC05-96OR22464
Resource Type:
Conference
Resource Relation:
Conference: SPIE Conference on Sensor Fusion and Decentralized Control in Robotic Systems II, Boston, MA (US), 09/19/1999--09/22/1999; Other Information: PBD: 19 Sep 1999
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; ALGORITHMS; LEARNING; PERFORMANCE; ROBOTS; COOPERATION; MULTI-ROBOT LEARNING; ADAPTATION; MULTI-ROBOT COOPERATION; ROBOT TEAMS

Citation Formats

Parker, L.E. A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams. United States: N. p., 1999. Web.
Parker, L.E. A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams. United States.
Parker, L.E. Sun . "A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams". United States. https://www.osti.gov/servlets/purl/14971.
@article{osti_14971,
title = {A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams},
author = {Parker, L.E.},
abstractNote = {While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {9}
}

Conference:
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