This thesis presents an imitation-based methodology, also a simple and easy way, for a service robot to learn the behaviors demonstrated by the user. With this proposed method, a robot can learn human behavior through observation. Inspired by the concept of biological learning, this learning model is initiated when facing a new learning event. A series of experiments are conducted to use a humanoid robot as a platform to implement the proposed algorithm. Discussions are made of how the robot generates a complete behavior sequences performed by its demonstrator. Because it is time consuming for a robot to go through the whole process of learning, we thus propose a decomposed learning method to enhance the learning performance, that is, based on the past learning information, the robot can skip learning again the behaviors already known.
For simple robot behaviors, a hierarchical evolutionary mechanism is developed to evolve the complete behavior trajectories. For complex behaviors sequences, different ways are used to tackle the scalability problem, including decomposing the overall task into several sub-tasks, exploiting behavior information recorded previously, and constructing a new strategy to maintain population diversity. To verify our approach, a different series of experiments have been conducted. The results show that our imitation-based approach is a natural way to teach the robot new behaviors. This evolutionary mechanism successfully enables a humanoid robot to perform the behavior sequences it learns.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0729109-002840 |
| Date | 29 July 2009 |
| Creators | Chung, Chi-Hsiu |
| Contributors | Chia-Mei Chen, Wei - Bo Lee, Bing - chiang Jeng |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840 |
| Rights | unrestricted, Copyright information available at source archive |
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