Planning for human-robot teaming in open worlds

Kartik Talamadupula, J. Benton, Subbarao Kambhampati, Paul Schermerhorn, Matthias Scheutz

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


As the number of applications for human-robot teaming continue to rise, there is an increasing need for planning technologies that can guide robots in such teaming scenarios. In this article, we focus on adapting planning technology to Urban Search And Rescue (USAR) with a human-robot team. We start by showing that several aspects of state-of-the-art planning technology, including temporal planning, partial satisfaction planning, and replanning, can be gainfully adapted to this scenario. We then note that human-robot teaming also throws up an additional critical challenge, namely, enabling existing planners, which work under closed-world assumptions, to cope with the open worlds that are characteristic of teaming problems such as USAR. In response, we discuss the notion of conditional goals, and describe how we represent and handle a specific class of them called open world quantified goals. Finally, we describe how the planner, and its open world extensions, are integrated into a robot control architecture, and provide an empirical evaluation over USAR experimental runs to establish the effectiveness of the planning components.

Original languageEnglish (US)
Article number1869403
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
StatePublished - Nov 2010


  • Automated planning
  • Planner
  • Robot
  • Search and rescue

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Artificial Intelligence


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