Abstract
Intelligent behaviors generally mean actions showing their objectives and proper sequences. For robot, to complete a given task properly, an intelligent computational model is necessary. Recurrent Neural Network (RNN) is one of the plausible computational models because the RNN can learn from previous experiences and memorize those experiences represented by inner state within the RNN. There are other computational models like hidden Markov model (HMM) and Support Vector Machine, but they are absent of continuity and inner state. In this paper, we tested several intelligent capabilities of the RNN, especially for memorization and generalization even under kidnapped situations, by simulating mobile robot in the experiments.
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Acknowledgment
This work was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A10051551).
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Lee, YM., Kim, JH. (2017). Trajectory Generation Using RNN with Context Information for Mobile Robots. In: Kim, JH., Karray, F., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-319-31293-4_2
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DOI: https://doi.org/10.1007/978-3-319-31293-4_2
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