Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Circular nodes in neural networks
Neural Computation
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
Limitations of nonlinear PCA as performed with generic neural networks
IEEE Transactions on Neural Networks
Using eigenposes for lossless periodic human motion imitation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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This paper proposes a new algorithm for the automatic segmentation of motion data from a humanoid soccer playing robot that allows feed-forward neural networks to generalize and reproduce various kinematic patterns, including walking, turning, and sidestepping. Data from a 20 degree-of-freedom Fujitsu hoap-1 robot is reduced to its intrinsic dimensionality, as determined by the isomap procedure, by means of nonlinear principal component analysis (nlpca). The proposed algorithm then automatically segments motion patterns by incrementally generating periodic temporally-constrained nonlinear pca neural networks and assigning data points to these networks in a conquer-and-divide fashion, that is, each network's ability to learn the data influences the data's division among the networks. The learned networks abstract five out of six types of motion without any prior information about the number or type of motion patterns. The multiple decoding subnetworks that result can serve to generate abstract actions for playing soccer and other complex tasks.