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Alignment-based transfer learning for robot models
2013
The 2013 International Joint Conference on Neural Networks (IJCNN)
Robot manipulation tasks require on robot models. When exact physical parameters of the robot are not available, learning robot models from data becomes an appealing alternative. Most learning approaches are formulated in a supervised learning framework and are based on clearly defined training sets. We propose a method that improves the learning process by using additional data obtained from other experiments of the robot or even from experiments with different robot architectures.
doi:10.1109/ijcnn.2013.6706721
dblp:conf/ijcnn/BocsiCP13
fatcat:3k3hbc6mkva3dc2fms3w5rnjwe