Autonomous System Control of Multiple Robotic Arms Collaboration via Machine Learning

Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms. Thus, incentivizing collaboration and preventing collisions are the two principles which are followed by the agent during the training process. Nowadays, more and more applications, both in industry and daily lives, require at least two arms, instead of requiring only a single arm. A dual-arm robot satisfies much more needs of different types of tasks, such as folding clothes at home, making a hamburger in a grill or picking and placing a product in a warehouse. The applications done in this paper are all about object pushing. This thesis focuses on how to train the agent to learn pushing an object away as far as possible. Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then utilized in this paper to train the agent to generate optimal actions. Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL methods used in this thesis.