TY - GEN
T1 - Leaning Impedance Distribution of Object from Images Using Fully Convolutional Neural Networks
AU - Kamigaki, Masahiro
AU - Muramatsu, Hisayoshi
AU - Katsura, Seiichiro
N1 - Funding Information:
This research was partially supported by the Ministry of Internal Affairs and Communications, Strategic Information and Communications R&D Promotion Programme (SCOPE), 201603011, 2020.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/18
Y1 - 2020/10/18
N2 - Robots have been introduced into industrial factory automation. It is necessary to consider interactions between the robots and environments to expand executable tasks of the robots. In the interaction, impedance is an essential factor for the robot to contact with the environment, whereas the impedance is unobservable without contact. In this study, we introduce a concept of affordance for impedance estimation without contact. We propose the impedance estimation method from an RGB image input using deep learning. In this paper, we show that the proposed method can extract pixels corresponding to sponges with its impedance composed of stiffness and viscosity, including the distribution of the impedance. We conducted the experiments to validate the proposed method.
AB - Robots have been introduced into industrial factory automation. It is necessary to consider interactions between the robots and environments to expand executable tasks of the robots. In the interaction, impedance is an essential factor for the robot to contact with the environment, whereas the impedance is unobservable without contact. In this study, we introduce a concept of affordance for impedance estimation without contact. We propose the impedance estimation method from an RGB image input using deep learning. In this paper, we show that the proposed method can extract pixels corresponding to sponges with its impedance composed of stiffness and viscosity, including the distribution of the impedance. We conducted the experiments to validate the proposed method.
KW - Environmental impedance estimation
KW - deep learning
KW - visual affordance
UR - http://www.scopus.com/inward/record.url?scp=85097780706&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097780706&partnerID=8YFLogxK
U2 - 10.1109/IECON43393.2020.9254319
DO - 10.1109/IECON43393.2020.9254319
M3 - Conference contribution
AN - SCOPUS:85097780706
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 2662
EP - 2667
BT - Proceedings - IECON 2020
PB - IEEE Computer Society
T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Y2 - 19 October 2020 through 21 October 2020
ER -