Copyright © 2006 Elsevier Ltd All rights reserved.
2006 Special issue
Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model
Available online 17 April 2006.
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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 network 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 position 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 different 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 mechanisms for those observed situated behaviors of the robot
Keywords: Learning of object handling behavior; Dynamical systems approach; Recurrent neural network
Article Outline
- 1. Introduction
- 2. Mechanism, model and algorithm
- 2.1. The basic mechanism
- 2.2. Model and algorithm
- 2.2.1. The architecture
- 2.2.2. Learning process
- 2.2.3. Generation of behaviors
- 3. The robot system configuration
- 4. Robot experiments
- 4.1. Dynamic generation of ball handling behaviors
- 4.1.1. Learning of behaviors from human direct teaching
- 4.1.2. Dynamic generation and switching of learned behaviors
- 4.1.3. Analysis of the memory structure of RNNPB
- 4.2. Interactive generation of block handling behaviors
- 5. Discussion
- 5.1. Dynamical systems explanation
- 5.2. Comparison with related works
- 5.3. Imitation learning with more natural setting
- 6. Summary
- Acknowledgements
- References






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