%0 Journal Article %1 Ito:2006 %A Ito, M. %A Noda, K. %A Hoshino, Y. %A Tani, J. %D 2006 %J Neural Networks %K approach, behavior, dynamical handling learning network neural object of recurrent systems %P 323-337 %T Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model %V 19 %X This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural net- work with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative po- sition between the robot’s hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned dif- ferent block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mech- anisms for those observed situated behaviors of the robot.

@article{Ito:2006, abstract = {This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural net- work with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative po- sition between the robot’s hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned dif- ferent block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mech- anisms for those observed situated behaviors of the robot.}, added-at = {2009-06-26T15:25:19.000+0200}, author = {Ito, M. and Noda, K. and Hoshino, Y. and Tani, J.}, biburl = {https://www.bibsonomy.org/bibtex/270d45a77b4cd43244bc22d74e2dcd5ad/butz}, description = {diverse cognitive systems bib}, interhash = {72c7bd167c1ee8788150117c25b4606d}, intrahash = {70d45a77b4cd43244bc22d74e2dcd5ad}, journal = {Neural Networks}, keywords = {approach, behavior, dynamical handling learning network neural object of recurrent systems}, owner = {butz}, pages = {323-337}, timestamp = {2009-06-26T15:25:38.000+0200}, title = {Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model}, volume = 19, year = 2006 }