Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation

Tomoya Kitamura; Atsumi Saito; Keisuke Yamazaki; Yuki Saito; Hiroshi Asai; Kouhei Ohnishi

doi:10.1109/IECON49645.2022.9968845

Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation

Tomoya Kitamura, Atsumi Saito, Keisuke Yamazaki, Yuki Saito, Hiroshi Asai, Kouhei Ohnishi

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Robots are being developed to perform tasks in homes and factories autonomously. Several studies have examined motion generation based on haptic information, and some studies consider the environment as physical property information. However, there is a trade-off between the accuracy and the time required for the physical property estimation. Therefore, in this study, we propose a method for estimating physical properties based on training the relationship between the two estimation models. The first is a fast sequential estimation model, and the second is a highly accurate posterior estimation model. Training the relationship between the two models makes highly accurate sequential property estimation possible. Validation results showed improved accuracy of property estimation for learning samples and some untrained samples.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9968845
Publication status	Published - 2022
Event	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2022-October
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22

Keywords

Haptics
model bridge
property estimation
system identification

Access to Document

10.1109/IECON49645.2022.9968845

Cite this

Kitamura, T., Saito, A., Yamazaki, K., Saito, Y., Asai, H., & Ohnishi, K. (2022). Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/IECON49645.2022.9968845

@inproceedings{35e2449772c5406d85efe5fbe249b846,

title = "Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation",

abstract = "Robots are being developed to perform tasks in homes and factories autonomously. Several studies have examined motion generation based on haptic information, and some studies consider the environment as physical property information. However, there is a trade-off between the accuracy and the time required for the physical property estimation. Therefore, in this study, we propose a method for estimating physical properties based on training the relationship between the two estimation models. The first is a fast sequential estimation model, and the second is a highly accurate posterior estimation model. Training the relationship between the two models makes highly accurate sequential property estimation possible. Validation results showed improved accuracy of property estimation for learning samples and some untrained samples.",

keywords = "Haptics, model bridge, property estimation, system identification",

author = "Tomoya Kitamura and Atsumi Saito and Keisuke Yamazaki and Yuki Saito and Hiroshi Asai and Kouhei Ohnishi",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9968845",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society",

}

Kitamura, T, Saito, A, Yamazaki, K, Saito, Y, Asai, H & Ohnishi, K 2022, Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), vol. 2022-October, IEEE Computer Society, 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968845

Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation. / Kitamura, Tomoya; Saito, Atsumi; Yamazaki, Keisuke et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2022. (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation

AU - Kitamura, Tomoya

AU - Saito, Atsumi

AU - Yamazaki, Keisuke

AU - Saito, Yuki

AU - Asai, Hiroshi

AU - Ohnishi, Kouhei

PY - 2022

Y1 - 2022

N2 - Robots are being developed to perform tasks in homes and factories autonomously. Several studies have examined motion generation based on haptic information, and some studies consider the environment as physical property information. However, there is a trade-off between the accuracy and the time required for the physical property estimation. Therefore, in this study, we propose a method for estimating physical properties based on training the relationship between the two estimation models. The first is a fast sequential estimation model, and the second is a highly accurate posterior estimation model. Training the relationship between the two models makes highly accurate sequential property estimation possible. Validation results showed improved accuracy of property estimation for learning samples and some untrained samples.

AB - Robots are being developed to perform tasks in homes and factories autonomously. Several studies have examined motion generation based on haptic information, and some studies consider the environment as physical property information. However, there is a trade-off between the accuracy and the time required for the physical property estimation. Therefore, in this study, we propose a method for estimating physical properties based on training the relationship between the two estimation models. The first is a fast sequential estimation model, and the second is a highly accurate posterior estimation model. Training the relationship between the two models makes highly accurate sequential property estimation possible. Validation results showed improved accuracy of property estimation for learning samples and some untrained samples.

KW - Haptics

KW - model bridge

KW - property estimation

KW - system identification

UR - http://www.scopus.com/inward/record.url?scp=85143886367&partnerID=8YFLogxK

U2 - 10.1109/IECON49645.2022.9968845

DO - 10.1109/IECON49645.2022.9968845

M3 - Conference contribution

AN - SCOPUS:85143886367

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Validation of a Property Estimation Method Based on Sequential and Posteriori Estimation

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this