Sim2Rob—A Sim2Real Framework for Smart Robotics in Assembly

Springer eBooks, pp. 331–342

Abstract

Abstract Robot-based assembly automation struggles with flexibility and adaptability in today's dynamic production landscape. Intelligent, AI-based robotic systems bring great potential for addressing those challenges as well as raising the level of automation. However, the implementation of powerful deep learning (DL) algorithms poses the so-called data problem. Acquiring real, annotated training data sets of sufficient quality and quantity usually requires a significant manual effort. Realistic simulation environments open up the possibility of generating synthetic training data in order to train models for real-world application. Combined with techniques for stable Sim2Real transfer to address the domain gap, this enables rapid adaptation to new products and processes. This work proposes a novel holistic framework for autonomous assembly based on synthetic data generation that leverages the synergy of lightweight robots, AI and advanced simulation capabilities while incorporating domain knowledge.

Authors 3

1. Oliver Petrović corresponding Aachen Laboratory for Machine Tools WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools WZL, RWTH Aachen University, Aachen, Germany

2. Petar Tesic Aachen Laboratory for Machine Tools WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools WZL, RWTH Aachen University, Aachen, Germany

3. Christian Brecher Aachen Laboratory for Machine Tools WZL

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools WZL, RWTH Aachen University, Aachen, Germany