Kurzbeschreibung (Abstract)
In the practice of motor skills in general, errors in the execution of movements may go
unnoticed when a human instructor is not available. In this case, a computer system or
robotic device able to detectmovement errors and propose corrections would be of great
help. This paper addresses the problem of how to detect such execution errors and how
to provide feedback to the human to correct his/her motor skill using a general, principled
methodology based on imitation learning. The core idea is to compare the observed
skill with a probabilistic model learned from expert demonstrations. The intensity of the
feedback is regulated by the likelihood of the model given the observed skill. Based on
demonstrations, our system can, for example, detect errors in the writing of characters
with multiple strokes. Moreover, by using a haptic device, the Haption Virtuose 6D,
we demonstrate a method to generate haptic feedback based on a distribution over
trajectories, which could be used as an auxiliary means of communication between an
instructor and an apprentice. Additionally, given a performance measurement, the haptic
device can help the human discover and performbettermovements to solve a given task.
In this case, the human first tries a few times to solve the task without assistance. Our
framework, in turn, uses a reinforcement learning algorithm to compute haptic feedback,
which guides the human toward better solutions.
| Typ des Eintrags: |
Artikel
|
| Erschienen: |
2018 |
| Autor(en): |
Ewerton, Marco ; Rother, David ; Weimar, Jakob ; Kollegger, Gerrit ; Wiemeyer, Josef ; Peters, Jan ; Maeda, Guilherme |
| Art des Eintrags: |
Zweitveröffentlichung |
| Titel: |
Assisting Movement Training and Execution With Visual and Haptic Feedback |
| Sprache: |
Englisch |
| Publikationsjahr: |
2018 |
| Publikationsdatum der Erstveröffentlichung: |
2018 |
| Verlag: |
Frontiers |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: |
Frontiers in Neurorobotics |
| Jahrgang/Volume einer Zeitschrift: |
12 |
| DOI: |
10.3389/fnbot.2018.00024 |
| URL / URN: |
https://doi.org/10.3389/fnbot.2018.00024 |
| Herkunft: |
Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): |
In the practice of motor skills in general, errors in the execution of movements may go
unnoticed when a human instructor is not available. In this case, a computer system or
robotic device able to detectmovement errors and propose corrections would be of great
help. This paper addresses the problem of how to detect such execution errors and how
to provide feedback to the human to correct his/her motor skill using a general, principled
methodology based on imitation learning. The core idea is to compare the observed
skill with a probabilistic model learned from expert demonstrations. The intensity of the
feedback is regulated by the likelihood of the model given the observed skill. Based on
demonstrations, our system can, for example, detect errors in the writing of characters
with multiple strokes. Moreover, by using a haptic device, the Haption Virtuose 6D,
we demonstrate a method to generate haptic feedback based on a distribution over
trajectories, which could be used as an auxiliary means of communication between an
instructor and an apprentice. Additionally, given a performance measurement, the haptic
device can help the human discover and performbettermovements to solve a given task.
In this case, the human first tries a few times to solve the task without assistance. Our
framework, in turn, uses a reinforcement learning algorithm to compute haptic feedback,
which guides the human toward better solutions. |
| Status: |
Verlagsversion |
| URN: |
urn:nbn:de:tuda-tuprints-75662 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): |
600 Technik, Medizin, angewandte Wissenschaften > 600 Technik |
| Fachbereich(e)/-gebiet(e): |
20 Fachbereich Informatik |
| Hinterlegungsdatum: |
15 Jul 2018 19:57 |
| Letzte Änderung: |
05 Dez 2023 09:11 |
| PPN: |
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| Export: |
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| Suche nach Titel in: |
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Assisting Movement Training and Execution With Visual and Haptic Feedback. (deposited 15 Jul 2018 19:57)
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