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- Publisher Website: 10.1109/ICRA.2019.8794227
- Scopus: eid_2-s2.0-85071438969
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Conference Paper: Incremental learning of spatial-temporal features in human motion patterns with mixture model for planning motion of a collaborative robot in assembly lines
Title | Incremental learning of spatial-temporal features in human motion patterns with mixture model for planning motion of a collaborative robot in assembly lines |
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Authors | |
Issue Date | 2019 |
Citation | Proceedings - IEEE International Conference on Robotics and Automation, 2019, v. 2019-May, p. 7858-7864 How to Cite? |
Abstract | Collaborative robots are expected to work in cooperation with humans to improve productivity and maintain the quality of products. In the previous study, we have proposed an incremental learning system for adaptively scheduling a motion of the collaborative robot based on a worker's behavior. Although this system could model the worker's motion pattern precisely and robustly without collecting the worker's data in advance, it required two different models for modeling the worker's spatial and temporal features respectively and was not well considered for generalization. In this paper, we extend the previous incremental learning system by integrating the spatial and temporal models using a mixture model. In addition, we install a new incremental learning algorithm which improves a generalization capability of the mixture model and avoids overfitting in the situation where the prior information is limited. Implementing the proposed algorithm, we evaluate the effectiveness of the proposed system by experiments for several workers and for several assembly processes. |
Persistent Identifier | http://hdl.handle.net/10722/303012 |
ISSN | 2023 SCImago Journal Rankings: 1.620 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Kanazawa, Akira | - |
dc.contributor.author | Kinugawa, Jun | - |
dc.contributor.author | Kosuge, Kazuhiro | - |
dc.date.accessioned | 2021-09-07T08:43:01Z | - |
dc.date.available | 2021-09-07T08:43:01Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings - IEEE International Conference on Robotics and Automation, 2019, v. 2019-May, p. 7858-7864 | - |
dc.identifier.issn | 1050-4729 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303012 | - |
dc.description.abstract | Collaborative robots are expected to work in cooperation with humans to improve productivity and maintain the quality of products. In the previous study, we have proposed an incremental learning system for adaptively scheduling a motion of the collaborative robot based on a worker's behavior. Although this system could model the worker's motion pattern precisely and robustly without collecting the worker's data in advance, it required two different models for modeling the worker's spatial and temporal features respectively and was not well considered for generalization. In this paper, we extend the previous incremental learning system by integrating the spatial and temporal models using a mixture model. In addition, we install a new incremental learning algorithm which improves a generalization capability of the mixture model and avoids overfitting in the situation where the prior information is limited. Implementing the proposed algorithm, we evaluate the effectiveness of the proposed system by experiments for several workers and for several assembly processes. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings - IEEE International Conference on Robotics and Automation | - |
dc.title | Incremental learning of spatial-temporal features in human motion patterns with mixture model for planning motion of a collaborative robot in assembly lines | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICRA.2019.8794227 | - |
dc.identifier.scopus | eid_2-s2.0-85071438969 | - |
dc.identifier.volume | 2019-May | - |
dc.identifier.spage | 7858 | - |
dc.identifier.epage | 7864 | - |
dc.identifier.isi | WOS:000494942305105 | - |