Learning to generate proactive and reactive behavior using a dynamic neural network model with time-varying variance prediction mechanism

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This paper discusses a possible neurodynamic mechanism that enables self-organization of two basic behavioral modes, namely a 'proactive mode' and a 'reactive mode,' and of autonomous switching between these modes depending on the situation. In the proactive mode, actions are generated based on an internal prediction, whereas in the reactive mode actions are generated in response to sensory inputs in unpredictable situations. In order to investigate how these two behavioral modes can be self-organized and how autonomous switching between the two modes can be achieved, we conducted neurorobotics experiments by using our recently developed dynamic neural network model that has a capability to learn to predict time-varying variance of the observable variables. In a set of robot experiments under various conditions, the robot was required to imitate other's movements consisting of alternating predictable and unpredictable patterns. The experimental results showed that the robot controlled by the neural network model was able to proactively imitate predictable patterns and reactively follow unpredictable patterns by autonomously switching its behavioral modes. Our analysis revealed that variance prediction mechanism can lead to self-organization of these abilities with sufficient robustness and generalization capabilities.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2014-10
Language
English
Article Type
Article
Keywords

HUMANOID ROBOT; MOTOR CONTROL; IMITATION; SYSTEMS; TASK

Citation

ADVANCED ROBOTICS, v.28, no.17, pp.1189 - 1203

ISSN
0169-1864
DOI
10.1080/01691864.2014.916628
URI
http://hdl.handle.net/10203/192845
Appears in Collection
EE-Journal Papers(저널논문)
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