TY - GEN
T1 - Success/Failure Identification of Skill Movement by Neural Network Using Force Information
AU - Sato, Koyo
AU - Oikawa, Masahide
AU - Kutsuzawa, Kyo
AU - Sakaino, Sho
AU - Tsuji, Toshiaki
N1 - Funding Information:
ACKNOWLEDGMENT This study was ?nancially supported by New Energy and Industrial Technology Development Organization (NEDO).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Currently, in the field of FA(Factory Automation), automation has been accomplished for only tasks with high reproducibility. Meanwhile, there remain a range of areas where tasks cannot reliably be performed due to their complexity or larger environmental variation. In these cases, the reliability of the task can be improved by recognizing the task failure through success/failure identifications and executing the task again when it fails. However, the success/failure identification using conventional machine learning methods has not been discussed for determining the success or failure for unlearned objects. Thus, this paper examined assembly tasks and demonstrated that the success or failure for an unlearned object can be identified by taking advantage of generalized nature of the neural network using force information. The results of making success/failure identifications using information on force, image, and position were compared and the advantage of force information in tasks was demonstrated.
AB - Currently, in the field of FA(Factory Automation), automation has been accomplished for only tasks with high reproducibility. Meanwhile, there remain a range of areas where tasks cannot reliably be performed due to their complexity or larger environmental variation. In these cases, the reliability of the task can be improved by recognizing the task failure through success/failure identifications and executing the task again when it fails. However, the success/failure identification using conventional machine learning methods has not been discussed for determining the success or failure for unlearned objects. Thus, this paper examined assembly tasks and demonstrated that the success or failure for an unlearned object can be identified by taking advantage of generalized nature of the neural network using force information. The results of making success/failure identifications using information on force, image, and position were compared and the advantage of force information in tasks was demonstrated.
KW - force information
KW - neural network
KW - success identification
UR - http://www.scopus.com/inward/record.url?scp=85084123863&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084123863&partnerID=8YFLogxK
U2 - 10.1109/IECON.2019.8927708
DO - 10.1109/IECON.2019.8927708
M3 - Conference contribution
AN - SCOPUS:85084123863
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 3641
EP - 3646
BT - Proceedings
PB - IEEE Computer Society
T2 - 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Y2 - 14 October 2019 through 17 October 2019
ER -