no code implementations • 4 Mar 2024 • Huy Le, Philipp Schillinger, Miroslav Gabriel, Alexander Qualmann, Ngo Anh Vien
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios.
no code implementations • 21 Sep 2023 • Yitian Shi, Philipp Schillinger, Miroslav Gabriel, Alexander Qualmann, Zohar Feldman, Hanna Ziesche, Ngo Anh Vien
Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i. e., objects that are unseen or out-of-domain (OOD), camera and bin settings, etc.
no code implementations • 31 Jul 2023 • Philipp Schillinger, Miroslav Gabriel, Alexander Kuss, Hanna Ziesche, Ngo Anh Vien
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups.
no code implementations • 16 Feb 2021 • Andras Kupcsik, Markus Spies, Alexander Klein, Marco Todescato, Nicolai Waniek, Philipp Schillinger, Mathias Buerger
In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs.
no code implementations • 24 Aug 2020 • Leonel Rozo, Meng Guo, Andras G. Kupcsik, Marco Todescato, Philipp Schillinger, Markus Giftthaler, Matthias Ochs, Markus Spies, Nicolai Waniek, Patrick Kesper, Mathias Büerger
Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations.