How multirobot systems research will accelerate our understanding of social animal behavior

Tucker Balch, Frank Dellaert, Adam Feldman, Andrew Guillory, Charles L. Isbell, Zia Khan, Stephen Pratt, Andrew N. Stein, Hank Wilde

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Our understanding of social insect behavior has significantly influenced artificial intelligence (AD and multirobot systems' research (e.g., ant algorithms and swarm, robotics). In this work, however, we focus on the opposite question: "How can multirobot systems research contribute to the understanding of social animal behavior?" As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects.

Original languageEnglish (US)
Pages (from-to)1445-1462
Number of pages18
JournalProceedings of the IEEE
Issue number7
StatePublished - Jul 2006


  • Multirobot systems
  • Social animals
  • Tracking

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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