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SKID RAW: Skill Discovery From Raw Trajectories

Tanneberg, Daniel ; Ploeger, Kai ; Rueckert, Elmar ; Peters, Jan (2022)
SKID RAW: Skill Discovery From Raw Trajectories.
In: IEEE Robotics and Automation Letters, 6 (3)
doi: 10.1109/LRA.2021.3068891
Artikel, Bibliographie

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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: Bibliographie
Titel: SKID RAW: Skill Discovery From Raw Trajectories
Sprache: Englisch
Publikationsjahr: 2022
Ort: Darmstadt
Verlag: IEEE
Titel der Zeitschrift, Zeitung oder Schriftenreihe: IEEE Robotics and Automation Letters
Jahrgang/Volume einer Zeitschrift: 6
(Heft-)Nummer: 3
DOI: 10.1109/LRA.2021.3068891
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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.

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: 02 Aug 2024 12:45
Letzte Änderung: 02 Aug 2024 12:45
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