Learning manipulation skills using deep reinforcement learning - Tan, Herman Jin Xing (Nanyang Technological University, 2021)
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.