基於深度學習類神經網路之機器人動作決策認知系統
| dc.contributor | 王偉彥 | zh_TW |
| dc.contributor | 許陳鑑 | zh_TW |
| dc.contributor | Wang, Wei-Yen | en_US |
| dc.contributor | Hsu, Chen-Chien | en_US |
| dc.contributor.author | 許閔傑 | zh_TW |
| dc.contributor.author | Hsu, Min-Jie | en_US |
| dc.date.accessioned | 2023-12-08T07:47:23Z | |
| dc.date.available | 2028-02-09 | |
| dc.date.available | 2023-12-08T07:47:23Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | none | zh_TW |
| dc.description.abstract | High-dimensional complex motion generation is an interesting research topic. Most action generation methods in robotics research use a single pose as the model output. However, in some scenarios, only a series of motions can be output at one time. The calligraphy writing task belongs to a complex motion generation challenge which needs to output a series of motions at one time. The calligraphy writing task can be divided into position learning and posture learning. For position learning, human can directly form a properly rational statement of where to write. In Taylor’s problem categories, the position learning problem in calligraphy learning belongs to Q3 and Q4 types which are formal statement. That is, human can easily design an algorithm to generate a policy to robot. In the contrast, humans are not able to describe the relationship between the writing posture and the writing result. Therefore, the posture learning problem in calligraphy learning belongs to Q1 and Q2 types in Taylor's problem categories. In order to solve the problems of Q1 and Q2, this dissertation will propose the fundamental cognitive system with self-learning ability. This dissertation integrates the framework of human perception, memory, and decision-making into the robot system through the cognitive psychology. We use the top-down and bottom-up processing of the human perceptual system to design a perception model of the cognitive system, which enables encoder networks to learn online. In the memorymodel, we implement the psychological multi-store model with a deep neural network, so that robots can remember past events like humans. We use the hypothesis generation model of psychology in the decision-making model, so that the robot has a human-like thinking process. Integrating these cognitive models, robots can generate action strategies based on their goals through their own experience. Finally, we use a practical robot as experimental platform to verify the learning ability of the proposed cognitive system. | en_US |
| dc.description.sponsorship | 電機工程學系 | zh_TW |
| dc.identifier | 80775003H-42852 | |
| dc.identifier.uri | https://etds.lib.ntnu.edu.tw/thesis/detail/20602136852db138df3aa6e95fd2bdd0/ | |
| dc.identifier.uri | http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/120355 | |
| dc.language | 英文 | |
| dc.subject | none | zh_TW |
| dc.subject | cognitive system | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | hypothesis generation model | en_US |
| dc.subject | memory model | en_US |
| dc.subject | perception model | en_US |
| dc.subject | Chinese calligraphy | en_US |
| dc.title | 基於深度學習類神經網路之機器人動作決策認知系統 | zh_TW |
| dc.title | Cognitive Systems with Robotic Motion Policies Based on Deep Learning Neural Networks | en_US |
| dc.type | etd |