Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning

Abstract

Individualized manufacturing is becoming an important approach to fulfill increasingly diverse consumer expectations. While there are various solutions for the manufacturing process, such as additive manufacturing, the subsequent automated assembly remains a challenging task. As an approach to this problem, we aim to teach a collaborative robot to successfully perform pick and place tasks by implementing reinforcement learning. For the assembly of an individualized product in a constantly changing manufacturing environment, the simulated geometric and dynamic parameters will be varied. Using reinforcement learning algorithms capable of meta-learning, the tasks will first be trained in simulation, and then performed in a real-world environment where new factors are introduced that were not simulated in training to confirm the robustness of the algorithms. A concept comprised of selected machine learning algorithms, hardware components as well as further research questions to realize the outlined production scenario are the results of the presented work.