| Project/Area Number |
62490011
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
広領域
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
SUZUKI Ryoji Univ.of Tokyo, Dept.of M.E.I.P., Prof., 工学部, 教授 (80013811)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
UNO Yoji Univ.of Tokyo, Dept.of M.E.I.P., Res.Ass., 工学部, 助手 (10203572)
川人 光男 大阪大学, 基礎工学部, 講師 (10144445)
|
|---|
| Project Period (FY) |
1987 – 1989
|
| Project Status |
Completed (Fiscal Year 1989)
|
| Budget Amount *help |
¥8,000,000 (Direct Cost: ¥8,000,000)
Fiscal Year 1989: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1988: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1987: ¥5,900,000 (Direct Cost: ¥5,900,000)
|
|---|
| 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 / 随意運動 / ニューラルネットワーク / 誤差逆伝播学習則 / 内部モデル / トルク変化最小軌道 / シナプス可塑性 / 隨意運動 / 学習制御 / トルク変化最小モデル / 多層神経回路 / 逆伝播学習法 |
|---|
| 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.
|
