no code implementations • 30 Apr 2024 • Benjamin Alt, Johannes Zahn, Claudius Kienle, Julia Dvorak, Marvin May, Darko Katic, Rainer Jäkel, Tobias Kopp, Michael Beetz, Gisela Lanza
While recent advances in deep learning have demonstrated its transformative potential, its adoption for real-world manufacturing applications remains limited.
no code implementations • 21 Apr 2024 • Benjamin Alt, Julia Dvorak, Darko Katic, Rainer Jäkel, Michael Beetz, Gisela Lanza
Over the past decade, deep learning helped solve manipulation problems across all domains of robotics.
no code implementations • 11 Jan 2023 • Chengzhi Wu, Kanran Zhou, Jan-Philipp Kaiser, Norbert Mitschke, Jan-Felix Klein, Julius Pfrommer, Jürgen Beyerer, Gisela Lanza, Michael Heizmann, Kai Furmans
To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed.
2 code implementations • MDPI 2022 • Marvin Carl May, Lars Kiefer, Andreas Kuhnle, Gisela Lanza
In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology.