A study of learning model of motor command and symbol for communication
| Project/Area Number |
14580413
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Nagaoka University of Technology |
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Principal Investigator |
WADA Yasuhiro Nagaoka University of Technology, Faculty of Engineering, Associate Professor, 工学部, 助教授 (70293248)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2004: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2003: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2002: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | trajectory formation / arm posture / motor command / intention / learning model for motor control / reinforcement learning / via-point / movement pattern / 予測規範 / シンボル |
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| Research Abstract |
The following two characteristics have been well demonstrated about the feature of a point-to-point human arm movement on a plane. Several models have been proposed to explain the features. Four criteria based on optimal principles have been proposed. In the project, we measured point-to-point movements in three-dimensional space and performed comparative examinations quantitatively between arm posture planned by the optimal criterion and measuerd arm posture. Next, we performed comparative examinations between the measured trajectories and the optimal trajectories based on minimum angle jerk criterion, minimum torque change criterion and minimum commanded torque change criterion. Finally, we report that both of the measured hand trajectory and arm posture were closest to the trajectory and posture predicted by the minimum commanded torque change criterion.
Humans can communicate using complex movements. However, the focus of the proposed models has been on transformation from movement trajectories to representations and on the regeneration of movements from representations. In other words, the models can not emulate the human representation acquirement process which includes lot of trial and error. We study a requirement process for complex movement patterns and symbols. We propose two models, one is a model that estimates the stochastic representation of a movement such as via-points, and the other is a model in which an estimated representation is related to a symbol. The first proposed model can acquire a set of via-points to regenerate an original pattern smoothly using on iterative process. In learning experiments, we tried to acquire movement patterns in via-point representation. As the result of experiments, we confirmed that our model can acquire representation that enables it to regenerate the example patterns. We confirmed that our second model can connect with symbols and patterns. The models can provide a fundamental model for communication.
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Report
(4 results)
Research Products
(17 results)
