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یادگیری فعالیت‌های عملکردی بازوی رباتیک از طریق نمایش مبتنی بر یادگیری تقویتی
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یادگیری فعالیت‌های عملکردی بازوی رباتیک از طریق نمایش مبتنی بر یادگیری تقویتی

نوحیان، امیر حسین Noohian, Amir Hossein

Robotic Arm Manipulation Learning from Demonstration based on Reinforcement Learning

Noohian, Amir Hossein | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54965 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Khodaygan, Saeed
  7. Abstract:
  8. The field of learning from demonstration is the field in which researchers seek to create methods by which a robot can learn and reproduce a skill simply by using the demonstration of the skill. One of the main drawbacks of learning from demonstration methods is their inability to improve the learned skills. To answer this question, the reinforcement learning method can be used. The reinforcement learning approach has the potential to improve the initial skill due to the use of the experience of interacting with the environment. In this project, the dynamic movement primitives algorithm is considered as the learning from demonstration method. The research approach is that first, the dynamic movement primitives are modeled in the framework of deep reinforcement learning. After learning the dynamic movement primitives is done by using deep reinforcement learning, a multi-objective deep reinforcement learning method is used to improve the initial trajectory. Finally, after evaluating the performance of the proposed algorithm, a case study of the performance of the robotic arm in performing the pick-and-placement operation in MuJoCo software environment is performed. In this case study, the robotic arm successfully learns a trajectory as a dynamic movement primitive and reproduces it at a lower speed
  9. Keywords:
  10. Learning from Demonstration (LfD) ; Deep Reinforcement Learning ; Multi-Objective Reinforcement Learning ; Dynamic Movement Primitives ; Robot Arm

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