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Autonomous Robots
Erschienen in: Autonomous Robots 3/2017

10.03.2016

Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

verfasst von: Guilherme J. Maeda, Gerhard Neumann, Marco Ewerton, Rudolf Lioutikov, Oliver Kroemer, Jan Peters

Erschienen in: Autonomous Robots | Ausgabe 3/2017

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Abstract

This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human–robot movement coordination. It uses imitation learning to construct a mixture model of human–robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human–robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories.
Vorheriger Artikel Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance
Nächster Artikel Impact of sensory preferences of individuals with autism on the recognition of emotions expressed by two robots, an avatar, and a human

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Fußnoten
1
For example, a rate of 1.25 acts as a surrogate for a human that moves 25 % slower than the time-aligned interaction model.
 
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Metadaten
Titel
Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks
verfasst von
Guilherme J. Maeda
Gerhard Neumann
Marco Ewerton
Rudolf Lioutikov
Oliver Kroemer
Jan Peters
Publikationsdatum
10.03.2016
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3/2017
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-016-9556-2

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