| Presentation | 1998/10/24 Inverse Kinematics Model Learning by Modular Architecture Networks Eimei Oyama, Susumu Tachi, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Inverse kinematics computation by using artificial neural networks that learn the inverse kinematics model of a robot arm has been proposed. However, the conventional methods do not pay enough attention on the discontinuity of the inverse kinematics system. It is difficult for the popular multi-layer neural network to approximate the discontinuous inverse kinematics system. In this paper, a novel modular neural net architecture for the inverse model learning is proposed. The inverse kinematics system can be approximated by the appropriate mixture of continuous functions. The modular networks can learn the discontinuous inverse kinematics system by appropriate switching of multiple continuous neural networks. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | neural networks / robot control / inverse kinematics / modular networks / sensorimotor integration |
| Paper # | NC98-45 |
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| Committee | NC |
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| Conference Date | 1998/10/24(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Inverse Kinematics Model Learning by Modular Architecture Networks |
| Sub Title (in English) | |
| Keyword(1) | neural networks |
| Keyword(2) | robot control |
| Keyword(3) | inverse kinematics |
| Keyword(4) | modular networks |
| Keyword(5) | sensorimotor integration |
| 1st Author's Name | Eimei Oyama |
| 1st Author's Affiliation | Mechanical Engineering Laboratory() |
| 2nd Author's Name | Susumu Tachi |
| 2nd Author's Affiliation | Faculty of Engineering, The University of Tokyo |
| Date | 1998/10/24 |
| Paper # | NC98-45 |
| Volume (vol) | vol.98 |
| Number (no) | 365 |
| Page | pp.pp.- |
| #Pages | 8 |
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