| Project/Area Number |
10450095
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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 |
Intelligent mechanics/Mechanical systems
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| Research Institution | The University of Tokyo |
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Principal Investigator |
YOSHIMOTO Ken-ichi Dept. of Mechano-Informatics, Univ. of Tokyo, Professor, 大学院・工学系研究科, 教授 (10011074)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAMURA Yoshihiko Dept. of Mechano-Informatics, Univ. of Tokyo, Professor, 大学院・工学系研究科, 教授 (20159073)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥12,200,000 (Direct Cost: ¥12,200,000)
Fiscal Year 1999: ¥4,900,000 (Direct Cost: ¥4,900,000)
Fiscal Year 1998: ¥7,300,000 (Direct Cost: ¥7,300,000)
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| Keywords | Motion primitive / Brain-like information processing / Short term and long term memory / Mimesis / Probabilistic neural network / Hidden Markov model / ヒューマノイド / センサー / 行動発現 / 非線形力学 |
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| Research Abstract |
The main results of this research are summarized as in the following four paragraphs :
(1) Development of a Multi-Sensored Torso Robot
A torso robot with dual 7 DOF manipulators with a three-fingered hand each was fabricated and equipped with multi-sensory devices. Namely, six of three-axes finger-tip force sensors, a six-axes wrist force/torque Sensor, joint angle Sensors, and dual CCD cameras whose images are sent to two different image processing systems.
(2) Grasp With Reactive Behaviors' Network
A learning algorithm was developed hr the internal parameters of the reactive behavior's network. The primitive reactive behaviors were designed and programmed using the torso robot and the multi-Sensory system. The algorithm was then installed into the torso robot and applied to the reactive grasp.
(3) Brain-Like Information Processing System with the Dual-Storage Memory Model
The associative memory model with the long-term and short-term storage was designed using neural networks and implemented for the torso model. In the experiments, ten sequential placements of four colored blocks were shown and taught to the robot every three seconds while the internal rehearsal was repeated to strengthen the memory. The robot playbacked the sequence with the arm and fingers based on the associative memory, which indicated the features of human memory.
(4) Understanding the Others with the Self Motion Primitives
It is believed ill the brain science that the mimesis is the basement of symbol manipulation, language, and logical thinking of the human and the large primates. We investigated the implementation of mimesis using the motion primitives of humanoid robots. The probabilistic neural networks and the hidden Markov model were devised for the integration. The computer simulation of the whole body motion were executed and showed the fundamental effectiveness.
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