Title page for etd-0729109-002840


[Back to Results | New Search]

URN etd-0729109-002840
Author Chi-Hsiu Chung
Author's Email Address u9223034@yuntech.edu.tw
Statistics This thesis had been viewed 5643 times. Download 1473 times.
Department Information Management
Year 2008
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation
Date of Defense 2009-07-21
Page Count 89
Keyword
  • Genetic Algorithm
  • Imitation Learning
  • Robot Learning
  • Abstract 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.
    Advisory Committee
  • Bing - chiang Jeng - chair
  • Chia-Mei Chen - co-chair
  • Wei - Bo Lee - advisor
  • Files
  • etd-0729109-002840.pdf
  • indicate access worldwide
    Date of Submission 2009-07-29

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys