  Individualized manufacturing is becoming an important approach as a means to
fulfill increasingly diverse and specific consumer requirements and
expectations. While there are various solutions to the implementation of 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. They will then be performed in a real-world environment where new
factors are introduced that were not simulated in training to confirm the
robustness of the algorithms. The robot will gain its input data from tactile
sensors, area scan cameras, and 3D cameras used to generate heightmaps of the
environment and the objects. The selection of machine learning algorithms and
hardware components as well as further research questions to realize the
outlined production scenario are the results of the presented work.
