Machine learning for control of robotic arms

• Main Author: Wu, Xiaoyang
• Other Authors: Wen Bihan
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
• Online Access: https://hdl.handle.net/10356/165263
• Institution: Nanyang Technological University

Robots are seen as a promising tool to help humans improve their productivity and living standard, with the advancement of technology, the introduction of machine learning has led to new developments in robotic arm control solutions. There are several mainstream machine learning control robotic arm solutions such as learning from demonstration and reinforcement learning. This dissertation compares their respective advantages and disadvantages. Reinforcement learning-based algorithms can better face complex unknown scenarios and can be combined with migration learning to achieve a smooth transition of the model from the simulated environment to the real world. In this dissertation, based on a model-free deep reinforcement learning algorithm, we illustrate the related concepts involved and then verify its performance under light noise or irregular objects through experiments.