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

EasyChair Preprint no. 2630, version 1

Versions: 12history
8 pagesDate: February 10, 2020


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.

Keyphrases: assembly process, Assembly Task, collaborative robotics, individualized manufacturing, Individualized Product, machine learning, Machine Learning Algorithm, manufacturing scenario, meta-learning, place task, Real world environment, Reinforcement Learning, sub-goal

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Caterina Neef and Dario Luipers and Jan Bollenbacher and Christian Gebel and Anja Richert},
  title = {Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning},
  howpublished = {EasyChair Preprint no. 2630},

  year = {EasyChair, 2020}}
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