Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots.
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| Title: | Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots. |
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| Authors: | Ikeuchi, Katsushi1 katsuike@microsoft.com, Ma, Zhaoyuan2 zma3@wpi.edu, Yan, Zengqiang3 zyanad@connect.ust.hk, Kudoh, Shunsuke4 s-kudoh@uec.ac.jp, Nakamura, Minako5 nakamura.minako@ocha.ac.jp |
| Source: | International Journal of Computer Vision. Dec2018, Vol. 126 Issue 12, p1415-1429. 15p. 1 Color Photograph, 14 Diagrams, 2 Charts, 2 Graphs. |
| Subjects: | Humanoid robots, Labanotation, Image recognition (Computer vision), Pose estimation (Computer vision), Computer vision |
| Abstract: | We have been developing a paradigm that we call learning-from-observation for a robot to automatically acquire a robot program to conduct a series of operations, or for a robot to understand what to do, through observing humans performing the same operations. Since a simple mimicking method to repeat exact joint angles or exact end-effector trajectories does not work well because of the kinematic and dynamic differences between a human and a robot, the proposed method employs intermediate symbolic representations, tasks, for conceptually representing what-to-do through observation. These tasks are subsequently mapped to appropriate robot operations depending on the robot hardware. In the present work, task models for upper-body operations of humanoid robots are presented, which are designed on the basis of Labanotation. Given a series of human operations, we first analyze the upper-body motions and extract certain fixed poses from key frames. These key poses are translated into tasks represented by Labanotation symbols. Then, a robot performs the operations corresponding to those task models. Because tasks based on Labanotation are independent of robot hardware, different robots can share the same observation module, and only different task-mapping modules specific to robot hardware are required. The system was implemented and demonstrated that three different robots can automatically mimic human upper-body operations with a satisfactory level of resemblance. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 132835296 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ikeuchi%2C+Katsushi%22">Ikeuchi, Katsushi</searchLink><relatesTo>1</relatesTo><i> katsuike@microsoft.com</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhaoyuan%22">Ma, Zhaoyuan</searchLink><relatesTo>2</relatesTo><i> zma3@wpi.edu</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Zengqiang%22">Yan, Zengqiang</searchLink><relatesTo>3</relatesTo><i> zyanad@connect.ust.hk</i><br /><searchLink fieldCode="AR" term="%22Kudoh%2C+Shunsuke%22">Kudoh, Shunsuke</searchLink><relatesTo>4</relatesTo><i> s-kudoh@uec.ac.jp</i><br /><searchLink fieldCode="AR" term="%22Nakamura%2C+Minako%22">Nakamura, Minako</searchLink><relatesTo>5</relatesTo><i> nakamura.minako@ocha.ac.jp</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Dec2018, Vol. 126 Issue 12, p1415-1429. 15p. 1 Color Photograph, 14 Diagrams, 2 Charts, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Humanoid+robots%22">Humanoid robots</searchLink><br /><searchLink fieldCode="DE" term="%22Labanotation%22">Labanotation</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Pose+estimation+%28Computer+vision%29%22">Pose estimation (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We have been developing a paradigm that we call learning-from-observation for a robot to automatically acquire a robot program to conduct a series of operations, or for a robot to understand what to do, through observing humans performing the same operations. Since a simple mimicking method to repeat exact joint angles or exact end-effector trajectories does not work well because of the kinematic and dynamic differences between a human and a robot, the proposed method employs intermediate symbolic representations, tasks, for conceptually representing what-to-do through observation. These tasks are subsequently mapped to appropriate robot operations depending on the robot hardware. In the present work, task models for upper-body operations of humanoid robots are presented, which are designed on the basis of Labanotation. Given a series of human operations, we first analyze the upper-body motions and extract certain fixed poses from key frames. These key poses are translated into tasks represented by Labanotation symbols. Then, a robot performs the operations corresponding to those task models. Because tasks based on Labanotation are independent of robot hardware, different robots can share the same observation module, and only different task-mapping modules specific to robot hardware are required. The system was implemented and demonstrated that three different robots can automatically mimic human upper-body operations with a satisfactory level of resemblance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11263-018-1123-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1415 Subjects: – SubjectFull: Humanoid robots Type: general – SubjectFull: Labanotation Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Pose estimation (Computer vision) Type: general – SubjectFull: Computer vision Type: general Titles: – TitleFull: Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ikeuchi, Katsushi – PersonEntity: Name: NameFull: Ma, Zhaoyuan – PersonEntity: Name: NameFull: Yan, Zengqiang – PersonEntity: Name: NameFull: Kudoh, Shunsuke – PersonEntity: Name: NameFull: Nakamura, Minako IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 126 – Type: issue Value: 12 Titles: – TitleFull: International Journal of Computer Vision Type: main |
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