Learning manipulation skills using deep reinforcement learning
In recent years, the growth of robotic arms working in the manufacturing line has been significant. Industrial robots are usually semi-supervised by an operator, and this is a very mundane task. However, the ability to teach robotic arms to become autonomous has been a challenge. Therefore, the purp...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/149318 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In recent years, the growth of robotic arms working in the manufacturing line has been significant. Industrial robots are usually semi-supervised by an operator, and this is a very mundane task. However, the ability to teach robotic arms to become autonomous has been a challenge. Therefore, the purpose of this study is to remove the mundane tasks from the operator's work by teaching the robot to do pick-and-place tasks autonomously. The starting point proposed by this study is to teach the UR5 robotic arm to learn pick-and-place using Unity Technologies's simulation software and Machine Learning Toolkit called Unity Game Engine and ML-Agents, respectively. Besides, the report looks into incorporating inverse kinematics for joint rotation calculation to place the robotic arm's end effector to a destination. |
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