Koert, Dorothea ; Trick, Susanne ; Ewerton, Marco ; Lutter, Michael ; Peters, Jan (2022)
Online Learning of an Open-Ended Skill Library for Collaborative Tasks.
International Conference on Humanoid Robots (Humanoids). Beijing, China (06.11.2018-09.11.2018)
doi: 10.26083/tuprints-00020545
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
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Kurzbeschreibung (Abstract)
Intelligent robotic assistants can potentially improve the quality of life for elderly people and help them maintain their independence. However, the number of different and personalized tasks render pre-programming of such assistive robots prohibitively difficult. Instead, to cope with a continuous and open-ended stream of cooperative tasks, new collaborative skills need to be continuously learned and updated from demonstrations. To this end, we introduce an online learning method for a skill library of collaborative tasks that employs an incremental mixture model of probabilistic interaction primitives. This model chooses a corresponding robot response to a human movement where the human intention is extracted from previously demonstrated movements. Unlike existing batch methods of movement primitives for human-robot interaction, our approach builds a library of skills online, in an open-ended fashion and updates existing skills using new demonstrations. The resulting approach was evaluated both on a simple benchmark task and in an assistive human-robot collaboration scenario with a 7DoF robot arm.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Koert, Dorothea ; Trick, Susanne ; Ewerton, Marco ; Lutter, Michael ; Peters, Jan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Online Learning of an Open-Ended Skill Library for Collaborative Tasks |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | IEEE |
| Buchtitel: | 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | International Conference on Humanoid Robots (Humanoids) |
| Veranstaltungsort: | Beijing, China |
| Veranstaltungsdatum: | 06.11.2018-09.11.2018 |
| DOI: | 10.26083/tuprints-00020545 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20545 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Intelligent robotic assistants can potentially improve the quality of life for elderly people and help them maintain their independence. However, the number of different and personalized tasks render pre-programming of such assistive robots prohibitively difficult. Instead, to cope with a continuous and open-ended stream of cooperative tasks, new collaborative skills need to be continuously learned and updated from demonstrations. To this end, we introduce an online learning method for a skill library of collaborative tasks that employs an incremental mixture model of probabilistic interaction primitives. This model chooses a corresponding robot response to a human movement where the human intention is extracted from previously demonstrated movements. Unlike existing batch methods of movement primitives for human-robot interaction, our approach builds a library of skills online, in an open-ended fashion and updates existing skills using new demonstrations. The resulting approach was evaluated both on a simple benchmark task and in an assistive human-robot collaboration scenario with a 7DoF robot arm. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-205458 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| TU-Projekte: | EC/H2020|640554|SKILLS4ROBOTS |
| Hinterlegungsdatum: | 18 Nov 2022 13:59 |
| Letzte Änderung: | 21 Nov 2022 10:40 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Online Learning of an Open-Ended Skill Library for Collaborative Tasks. (deposited 18 Nov 2022 13:59) [Gegenwärtig angezeigt]
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