1989 Fiscal Year Final Research Report Summary
Neural Network Model for Voluntary Movement and Application to Robotics
| Project/Area Number |
62490011
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
広領域
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SUZUKI Ryoji Univ.of Tokyo, Dept.of M.E.I.P., Prof., 工学部, 教授 (80013811)
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| Co-Investigator(Kenkyū-buntansha) |
UNO Yoji Univ.of Tokyo, Dept.of M.E.I.P., Res.Ass., 工学部, 助手 (10203572)
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| Project Period (FY) |
1987 – 1989
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| Keywords | Voluntary Arm Movement / Neural Network / Manipulator Robot Control / Inverse Dynamics of Motor System / Inverse Kinematics of Motor System / Feedback-Error-Learning / Optimal Trajectory Formation / Minimum Torque-Change Criterion |
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| Research Abstract |
Human motor skills are not innate, but have to be acquired by training from birth. In the beginning, movements are controlled by using feedback signals through visual pathways. With increased skill, the feedback control system is replaced as the main controller by a feedforward control system which means movements can be control led unconsciously. A neural network model which can explain this process is proposed. The model is based on a rule called the feedback-error-learning. The inverse dynamics of the motor system is organized in a three layer neural network according to the back-propagation learning rule.
Optimal control of human arm movement is also discussed , and a neural network model which realizes the optimal pathways based on the minimum torque change criterion is proposed. Basic ideas are (1) spatial representation of time, (2) learning of forward dynamics and kinematics model and (3) relaxation computation based on the acquired model. Operations of this network are divided into the learning phase and the pattern-generating phase. In the learning phase, this network acquires a forward model of the multi-degree-of-freedom controlled object while monitoring the actual trajectory as a teaching signal. In the pattern-generating phase, electrical coupling between neurons representing motor commands at neighboring times is activated to guarantee the minimum torque-change criterion. By computer simulation, we show that the model can produce a multi-joint arm trajectory while avoiding obstacles or passing through viapoints.
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