TY - GEN
T1 - Body model transition by tool grasping during motor babbling using deep learning and RNN
AU - Takahashi, Kuniyuki
AU - Tjandra, Hadi
AU - Ogata, Tetsuya
AU - Sugano, Shigeki
N1 - Funding Information:
This work has been supported by JSPS Grant-in-Aid for Scientific Research 15J12683; the Program for Leading Graduate Schools, “Graduate Program for Embodiment Informatics” of the Ministry of Education, Culture, Sports, Science, and Technology; JSPS Grant-in-Aid for Scientific Research (S) (2522005); “Fundamental Study for Intelligent Machine to Coexist with Nature” Research Institute for Science and Engineering, Waseda University; MEXT Grant-in-Aid for Scientific Research (A) 15H01710; and MEXT Grant-in-Aid for Scientific Research on Innovative Areas “Constructive Developmental Science” (24119003)
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - We propose a method of tool use considering the transition process of a body model from not grasping to grasping a tool using a single model. In our previous research, we proposed a tool-body assimilation model in which a robot autonomously learns tool functions using a deep neural network (DNN) and recurrent neural network (RNN) through experiences of motor babbling. However, the robot started its motion already holding the tools. In real-life situations, the robot would make decisions regarding grasping (handling) or not grasping (manipulating) a tool. To achieve this, the robot performs motor babbling without the tool pre-attached to the hand with the same motion twice, in which the robot handles the tool or manipulates without graping it. To evaluate the model, we have the robot generate motions with showing the initial and target states. As a result, the robot could generate the correct motions with grasping decisions.
AB - We propose a method of tool use considering the transition process of a body model from not grasping to grasping a tool using a single model. In our previous research, we proposed a tool-body assimilation model in which a robot autonomously learns tool functions using a deep neural network (DNN) and recurrent neural network (RNN) through experiences of motor babbling. However, the robot started its motion already holding the tools. In real-life situations, the robot would make decisions regarding grasping (handling) or not grasping (manipulating) a tool. To achieve this, the robot performs motor babbling without the tool pre-attached to the hand with the same motion twice, in which the robot handles the tool or manipulates without graping it. To evaluate the model, we have the robot generate motions with showing the initial and target states. As a result, the robot could generate the correct motions with grasping decisions.
KW - Deep neural network
KW - Grasping
KW - Recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=84987995234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987995234&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-44778-0_20
DO - 10.1007/978-3-319-44778-0_20
M3 - Conference contribution
AN - SCOPUS:84987995234
SN - 9783319447773
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 174
BT - Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
A2 - Villa, Alessandro E.P.
A2 - Masulli, Paolo
A2 - Rivero, Antonio Javier Pons
PB - Springer Verlag
T2 - 25th International Conference on Artificial Neural Networks, ICANN 2016
Y2 - 6 September 2016 through 9 September 2016
ER -