@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.}
}