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Embedded Neural Network for Learning of Physical Robots
http://hdl.handle.net/10445/5282
http://hdl.handle.net/10445/5282323acc9d-d15b-4543-93d4-7c1361556035
| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2010-11-29 | |||||
| タイトル | ||||||
| タイトル | Embedded Neural Network for Learning of Physical Robots | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hartono, Pitoyo
× Hartono, Pitoyo× Kakita, Sachiko |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this study we ran real time learning of multiple physical autonomous robots situated in a real dynamic environment. Each robot has an onboard micro controller where a simple neural network is embedded. The neural network was built with the consideration of the power and calculation resources limitation which is a general characteristic of simple robots. In the experiments, several autonomous robots were placed in one environment, where each of them was given a specific task which was expressed as the evaluation function for the robot's neural network. The learning processes of the robots were started simultaneously from their randomized initial conditions. The presence of several robots consequently formed a dynamic environment, in which an action of one robot affected the learning process of others. We demonstrated the efficiency of the embedded learning mechanism with respect to different environmental factors. | |||||
| 書誌情報 |
Proc. Int. Conf. on Artificial Neural Networks (ICANN 2008) 巻 2, p. 141-149, 発行日 2008 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 国際会議/International Conference | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
| 出版者 | ||||||
| 出版者 | Springer LNCS 5163 | |||||
