2000 Fiscal Year Final Research Report Summary
Study on Motor Skill and Autonomous Intelligence in Human and Robot
| Project/Area Number |
10450165
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
ITO Koji Tokyo Institute of Technology, Professor, 大学院・総合理工学研究科, 教授 (30023310)
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| Co-Investigator(Kenkyū-buntansha) |
KONDO Toshiyuki Tokyo Institute of Technology, Assistant Professor, 大学院・総合理工学研究科, 助手 (60323820)
YAMASHITA Masaki Tokyo Institute of Technology, Associate Professor, 大学院・総合理工学研究科, 助教授 (30220247)
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| Project Period (FY) |
1998 – 2000
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| Keywords | Motor learning and control / Self-organization / Motor skill / Associative memory / Brain dynamics / Autonomous Robot |
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| Research Abstract |
We obtained very interesting results on motor skill and autonomous intelligence for human and robot, which will give a significant contribution toward developing home or pet robots in the future. The summary is as follows. (see the final report fro detail).
1. Motor : model The experimental analysis was performed in terms of the motor learning in the reaching motion of upper limb, and then it was suggested that the external environments (force field of viscosity) might be represented as a semi-local model in the brain.
2. Motor learning : Motor skill is acquired through the interaction with his environment. We proposed a new procedure to generate the motor pattern suitable for the task through the repetition of motion. It was successfully applied to the reaching and batting motions.
3. Dynamical associative memory model : The proposed associative memory model consists of structurally unstable oscillators and a common field such as chemical concentration. It is demonstrated that when a known pattern is given to the network, the internal state of the network results in the oscillatory one, and when an unknown pattern is given to the network, the internal state oscillates chaotically. Further, a new autonomous learning algorithm utilizing these dynamical characteristics was proposed. Computer simulation demonstrated that the additional learning did not destroy the previously memorized patterns.
4. Action learning for autonomous mobile robot : We proposed a new architecture for action learning by combining reinforcement learning with recurrent neural network. It was shown that the mapping from the sensor space to the motor torque could be acquired according to the task goal and the useful information to reach the goal could be obtained as an abstract representation in the middle layer of the neural network.
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