High Performance Execution of Everyday Pick-and-Place Tasks by Integrating Transformation Planning and Reactive Execution (bibtex)
by Lorenz Mösenlechner, Armin Müller, Michael Beetz
Abstract:
We investigate the plan-based control of physically and sensorically realistic simulated autonomous mobile robots performing everyday pick-and-place tasks in human environments, such as table setting. Our approach applies AI planning techniques to transform default plans that can be inferred from instructions for activities of daily life into flexible, high-performance robot plans. To find high performance plans the planning system applies transformations such as carrying plates to the table by stacking them or leaving cabinet doors open while setting the table, which require substantial changes of the control structure of the intended activities. We argue and demonstrate that applying AI planning techniques directly to concurrent reactive plan languages, instead of using layered software architectures with different languages, enables the robot action planner to achieve substantial performance improvements (23\% - 45\% depending on the tasks). We also argue that the transformation of concurrent reactive plans is necessary to obtain the results. Our claims are supported by extensive empirical investigations in realistic simulations.
Reference:
Lorenz Mösenlechner, Armin Müller, Michael Beetz, "High Performance Execution of Everyday Pick-and-Place Tasks by Integrating Transformation Planning and Reactive Execution", In Proceedings of the 1st International Workshop on Cognition for Technical Systems, München, Germany, 6-8 October, 2008.
Bibtex Entry:
@InProceedings{moesenle08cotesys,
  author    = {Lorenz M{\"o}senlechner and Armin M\"uller and Michael Beetz},
  title     = {High Performance Execution of Everyday Pick-and-Place Tasks by Integrating Transformation Planning and Reactive Execution},
  booktitle = {Proceedings of the 1st International Workshop on Cognition for Technical Systems, M{\"u}nchen, Germany, 6-8 October},
  year      = {2008},
  bib2html_pubtype = {Conference Paper},
  bib2html_groups  = {Cogito},
  bib2html_funding  = {CoTeSys},
  bib2html_rescat  = {Planning, Action},
  bib2html_domain  = {Assistive Household},
  abstract = {We investigate the plan-based control of physically and sensorically
              realistic simulated autonomous mobile robots performing everyday
              pick-and-place tasks in human environments, such as table setting.
              Our approach applies AI planning techniques to transform default
              plans that can be inferred from instructions for activities of daily
              life into flexible, high-performance robot plans. To find high
              performance plans the planning system applies transformations such
              as carrying plates to the table by stacking them or leaving cabinet doors
              open while setting the table, which require substantial changes of
              the control structure of the intended activities.

              We argue and demonstrate that applying AI planning techniques
              directly to concurrent reactive plan languages, instead of using
              layered software architectures with different languages, enables the
              robot action planner to achieve substantial performance improvements
              (23\% - 45\% depending on the tasks). We also argue that the
              transformation of concurrent reactive plans is necessary to obtain
              the results. Our claims are supported by extensive empirical
              investigations in realistic simulations.}
}
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