TY - GEN
T1 - Object Recognition Through Active Sensing Using a Multi-Fingered Robot Hand with 3D Tactile Sensors
AU - Funabashi, Satoshi
AU - Morikuni, Shu
AU - Geier, Andreas
AU - Schmitz, Alexander
AU - Ogasa, Shun
AU - Torno, Tito Pradhono
AU - Somlor, Sophon
AU - Sugano, Shigeki
N1 - Funding Information:
This research was partially supported by the JSPS Grantin-Aid for Scientific Research (S) No. JP25220005, JSPS Grantin-Aid for Young Scientists No. JP17K18183, and JSPS Grant-in-Aid for Young Scientists No. JP17J10795.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - This paper investigates tactile object recognition with relatively densely distributed force vector measurements and evaluates what kind of tactile information is beneficial for object recognition. The uSkin tactile sensors are embedded in an Allegro Hand, and provide 240 triaxial force vector measurements in total in all fingers. Active object sensing is used to gather time-series training and testing data. A simple feedforward, a recurrent, and a convolutional neural network are used for recognizing objects. Evaluations with different number of employed measurements, static vs. time series data and force vector vs. only normal force vector measurements show that the high-dimensional information provided by the sensors is indeed beneficial. An object recognition rate of up to 95% for 20 objects was achieved.
AB - This paper investigates tactile object recognition with relatively densely distributed force vector measurements and evaluates what kind of tactile information is beneficial for object recognition. The uSkin tactile sensors are embedded in an Allegro Hand, and provide 240 triaxial force vector measurements in total in all fingers. Active object sensing is used to gather time-series training and testing data. A simple feedforward, a recurrent, and a convolutional neural network are used for recognizing objects. Evaluations with different number of employed measurements, static vs. time series data and force vector vs. only normal force vector measurements show that the high-dimensional information provided by the sensors is indeed beneficial. An object recognition rate of up to 95% for 20 objects was achieved.
UR - http://www.scopus.com/inward/record.url?scp=85063012595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063012595&partnerID=8YFLogxK
U2 - 10.1109/IROS.2018.8594159
DO - 10.1109/IROS.2018.8594159
M3 - Conference contribution
AN - SCOPUS:85063012595
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2589
EP - 2595
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Y2 - 1 October 2018 through 5 October 2018
ER -