TY - GEN
T1 - Tool-body assimilation model based on body babbling and a neuro-dynamical system for motion generation
AU - Takahashi, Kuniyuki
AU - Ogata, Tetsuya
AU - Tjandra, Hadi
AU - Murata, Shingo
AU - Arie, Hiroaki
AU - Sugano, Shigeki
PY - 2014
Y1 - 2014
N2 - We propose a model for robots to use tools without predetermined parameters based on a human cognitive model. Almost all existing studies of robot using tool require predetermined motions and tool features, so the motion patterns are limited and the robots cannot use new tools. Other studies use a full search for new tools; however, this entails an enormous number of calculations. We built a model for tool use based on the phenomenon of tool-body assimilation using the following approach: We used a humanoid robot model to generate random motion, based on human body babbling. These rich motion experiences were then used to train a recurrent neural network for modeling a body image. Tool features were self-organized in the parametric bias modulating the body image according to the used tool. Finally, we designed the neural network for the robot to generate motion only from the target image.
AB - We propose a model for robots to use tools without predetermined parameters based on a human cognitive model. Almost all existing studies of robot using tool require predetermined motions and tool features, so the motion patterns are limited and the robots cannot use new tools. Other studies use a full search for new tools; however, this entails an enormous number of calculations. We built a model for tool use based on the phenomenon of tool-body assimilation using the following approach: We used a humanoid robot model to generate random motion, based on human body babbling. These rich motion experiences were then used to train a recurrent neural network for modeling a body image. Tool features were self-organized in the parametric bias modulating the body image according to the used tool. Finally, we designed the neural network for the robot to generate motion only from the target image.
KW - multiple time-scales recurrent neural network
KW - tool-body assimilation
UR - http://www.scopus.com/inward/record.url?scp=84958535712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958535712&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11179-7_46
DO - 10.1007/978-3-319-11179-7_46
M3 - Conference contribution
AN - SCOPUS:84958535712
SN - 9783319111780
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 363
EP - 370
BT - Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PB - Springer Verlag
T2 - 24th International Conference on Artificial Neural Networks, ICANN 2014
Y2 - 15 September 2014 through 19 September 2014
ER -