Tanneberg, Daniel ; Ploeger, Kai ; Rueckert, Elmar ; Peters, Jan (2022)
SKID RAW: Skill Discovery From Raw Trajectories.
In: IEEE Robotics and Automation Letters, 2022, 6 (3)
doi: 10.26083/tuprints-00020536
Artikel, Zweitveröffentlichung, Postprint
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Kurzbeschreibung (Abstract)
Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching them a new task in an easy and natural way. When teaching tasks that involve sequences of different skills, with varying order and number of these skills, it is desirable to only demonstrate full task executions instead of all individual skills. For this purpose, we propose a novel approach that simultaneously learns to segment trajectories into reoccurring patterns and the skills to reconstruct these patterns from unlabelled demonstrations without further supervision. Moreover, the approach learns a skill conditioning that can be used to understand possible sequences of skills, a practical mechanism to be used in, for example, human-robot-interactions for a more intelligent and adaptive robot behaviour. The Bayesian and variational inference based approach is evaluated on synthetic and real human demonstrations with varying complexities and dimensionality, showing the successful learning of segmentations and skill libraries from unlabelled data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Tanneberg, Daniel ; Ploeger, Kai ; Rueckert, Elmar ; Peters, Jan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | SKID RAW: Skill Discovery From Raw Trajectories |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | IEEE |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Robotics and Automation Letters |
| Jahrgang/Volume einer Zeitschrift: | 6 |
| (Heft-)Nummer: | 3 |
| DOI: | 10.26083/tuprints-00020536 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20536 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching them a new task in an easy and natural way. When teaching tasks that involve sequences of different skills, with varying order and number of these skills, it is desirable to only demonstrate full task executions instead of all individual skills. For this purpose, we propose a novel approach that simultaneously learns to segment trajectories into reoccurring patterns and the skills to reconstruct these patterns from unlabelled demonstrations without further supervision. Moreover, the approach learns a skill conditioning that can be used to understand possible sequences of skills, a practical mechanism to be used in, for example, human-robot-interactions for a more intelligent and adaptive robot behaviour. The Bayesian and variational inference based approach is evaluated on synthetic and real human demonstrations with varying complexities and dimensionality, showing the successful learning of segmentations and skill libraries from unlabelled data. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-205363 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| TU-Projekte: | EC/H2020|640554|SKILLS4ROBOTS |
| Hinterlegungsdatum: | 18 Nov 2022 13:41 |
| Letzte Änderung: | 21 Nov 2022 10:01 |
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| Suche nach Titel in: | TUfind oder in Google |
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- SKID RAW: Skill Discovery From Raw Trajectories. (deposited 18 Nov 2022 13:41) [Gegenwärtig angezeigt]
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