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Documents authored by Wachsmuth, Sven

Document
DOI: 10.4230/DagSemProc.10401.5
The Task-State Coordination Pattern, with applications in Human-Robot-Interaction

Authors: Ingo Lütkebohle, Julia Peltason, Britta Wrede, and Sven Wachsmuth

Published in: Dagstuhl Seminar Proceedings, Volume 10401, Learning, Planning and Sharing Robot Knowledge for Human-Robot Interaction (2011)

Abstract
We consider interaction a powerful enabling technology for robots in human environments. Besides taking commands or reporting, many other uses, such as interactive learning, are already being explored. However, HRI also poses systems engineering challenges that may hinder its adoption. To address these, we advocate a general coordination pattern for task execution: The Task-State Pattern. Crucially, it separates interaction coordination from task-level control, thus enabling independent, but integrated, development. In the pattern, tasks are represented using both a general, re-usable task coordination model and a task-type dependent specification. We have introduced a coordination model rich enough to support a powerful user experience, but still general enough to accomodate a variety of tasks, thus simplifying architecture and integration. Furthermore, because it is re-used in many places, it provides an attractive target for tool support.
Cite as

Ingo Lütkebohle, Julia Peltason, Britta Wrede, and Sven Wachsmuth. The Task-State Coordination Pattern, with applications in Human-Robot-Interaction. In Learning, Planning and Sharing Robot Knowledge for Human-Robot Interaction. Dagstuhl Seminar Proceedings, Volume 10401, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{lutkebohle_et_al:DagSemProc.10401.5,
  author =	{L\"{u}tkebohle, Ingo and Peltason, Julia and Wrede, Britta and Wachsmuth, Sven},
  title =	{{The Task-State Coordination Pattern, with applications in Human-Robot-Interaction}},
  booktitle =	{Learning, Planning and Sharing Robot Knowledge for Human-Robot Interaction},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10401},
  editor =	{Rachid Alami and R\"{u}diger Dillmann and Thomas C. Henderson and Alexandra Kirsch},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10401.5},
  URN =		{urn:nbn:de:0030-drops-29318},
  doi =		{10.4230/DagSemProc.10401.5},
  annote =	{Keywords: Coordination, software architecture, design pattern, dialog, human-robot-interaction}
}
Document
DOI: 10.4230/DagSemProc.08091.10
Probabilistic Scene Modeling for Situated Computer Vision

Authors: Sven Wachsmuth and Agnes Swadzba

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract
Verbal statements and vision are a rich source of information in a human-machine interaction scenario. For this reason Situated Computer Vision aims to include knowledge about the communicative situation in which it takes place. This paper presents three approaches how to achieve scene models of such scenarios combining different modalities. Seeing (planar) scenes as configurations of parts leads to a probabilistic modeling with Bayes’ nets relating spoken utterances with results of an object recognition step. In the second approach parallel datasets form the basis for analyzing the statistical dependencies between them through learning a statistical translation model which maps between these datasets (here: words in a text and boundary fragments extracted in 2D images). The third approach deals with complex indoor scenes from which 3D data is acquired. Planar structures in the 3D points and statistics extracted on these planar patches describe the coarse spatial layouts of different indoor room types in such a way that a holistic classification scheme can be provided.
Cite as

Sven Wachsmuth and Agnes Swadzba. Probabilistic Scene Modeling for Situated Computer Vision. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{wachsmuth_et_al:DagSemProc.08091.10,
  author =	{Wachsmuth, Sven and Swadzba, Agnes},
  title =	{{Probabilistic Scene Modeling for Situated Computer Vision}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.10},
  URN =		{urn:nbn:de:0030-drops-16097},
  doi =		{10.4230/DagSemProc.08091.10},
  annote =	{Keywords: Scene Modeling, Human Robot Interaction}
}
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