Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks

Jones, E. Gil; Browning, Brett; Dias, M. Bernardine; Argall, Brenna; Veloso, Manuela; Stentz, Anthony

doi:10.1109/robot.2006.1641771

Cited by 98 publications

(73 citation statements)

References 8 publications

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“…Our game is a variation on the treasure hunt domain introduced in Jones et al (2006). The original domain was designed to assess the performance of competitive "pick up" (i.e., ad hoc) teams of heterogenous robots exploring an unknown environment and searching for treasure.…”

Section: Experimental Domain: the Treasure Hunt Gamementioning

confidence: 99%

Argumentation-Based Dialogue Games for Shared Control in Human-Robot Systems

Sklar¹,

Azhar²

2015

Journal of Human-Robot Interaction

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Dialogue can support exchange of ideas and discussion of options as a means to enable shared decision making for human-robot collaboration. However, dialogue that supports dynamic, evidencebacked exchange of ideas is a major challenge for today's human-robot systems. The work presented here investigates the application of argumentation-based dialogue games as the means to facilitate flexible interaction, including unscripted changes in initiative. Two main contributions are provided in this paper. First, a methodology for implementing multiple types of argumentation-based dialogues for human-robot interaction is detailed. This includes explanation about which types of dialogues are appropriate given the beliefs of the participants and how multiple dialogues can occur simultaneously while maintaining a consistent set of beliefs for the participants. Second, a formal definition is presented for the Treasure Hunt Game (THG), a test environment that provides rich opportunities for experimentation in shared human-robot control, as well as motivating and engaging experiences for human subjects.

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Section: Experimental Domain: the Treasure Hunt Gamementioning

confidence: 99%

Argumentation-Based Dialogue Games for Shared Control in Human-Robot Systems

Sklar¹,

Azhar²

2015

Journal of Human-Robot Interaction

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“…Barrett et al present empirical evaluations of various types of ad hoc agents when joining coordinated teams of unknown agents [1]. Jones et al present a treasure hunt domain for evaluating ad hoc team performance and present a simple implementation of a team that can search for treasure in such a domain [3].…”

Section: Related Workmentioning

confidence: 99%

Benchmarking robot cooperation without pre-coordination in the RoboCup Standard Platform League drop-in player competition

Genter

Laue

Stone

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The Standard Platform League is one of the main competitions of the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2014, the league added a new subcompetition which serves as a testbed for cooperation without pre-coordination: the Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i. e. teams that consist of robots originating from different RoboCup teams and that are each running different software. In this paper, we provide an overview of this competition, including its motivation and rules. We then present and analyze the results of the 2014 competition, which gathered robots from 23 teams, involved at least 50 human participants, and consisted of fifteen 20-minute games for a total playing time of 300 minutes. We also suggest improvements for future iterations, many of which will be evaluated at RoboCup 2015.

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“…A significant amount of recent work addresses more powerful market-based approaches for task allocation [39], [58], [96], [117], [61], [11], [91], [127], [42], [52], [64], [32], [50], [85]. These approaches can deal with more complex task representations, more coupling between tasks, dynamic events, combinatorial auctions, and so forth.…”

Section: B Organizational Approach To Task Allocationmentioning

confidence: 99%

Distributed intelligence: overview of the field and its application in multi-robot systems

Parker¹

2008

jopha

151

107

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Abstract-This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the solution paradigm to be used. We outline three common paradigms for distributed intelligence -the bioinspired paradigm, the organizational and social paradigm, and the knowledge-based, ontological paradigm -and give examples of how these paradigms can be used in multi-robot systems. We then look at a common problem in multirobot systems -that of task allocation -and show how the solution approach to this problem is very different depending upon the paradigm chosen for abstracting the problem. Our conclusion is that the paradigms are not interchangeable, but rather the selection of the appropriate paradigm is dependent upon the specific constraints and requirements of the application of interest. Further work is needed to provide guidance to the system designer on selecting the proper abstraction, or paradigm, for a given problem.

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Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks

Cited by 98 publications

References 8 publications

Argumentation-Based Dialogue Games for Shared Control in Human-Robot Systems

Argumentation-Based Dialogue Games for Shared Control in Human-Robot Systems

Benchmarking robot cooperation without pre-coordination in the RoboCup Standard Platform League drop-in player competition

Distributed intelligence: overview of the field and its application in multi-robot systems

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