Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Kupcsik, A."'
Robot manipulation relying on learned object-centric descriptors became popular in recent years. Visual descriptors can easily describe manipulation task objectives, they can be learned efficiently using self-supervision, and they can encode actuated
Externí odkaz:
http://arxiv.org/abs/2406.12441
Autor:
Rozo, Leonel, Kupcsik, Andras G., Schillinger, Philipp, Guo, Meng, Krug, Robert, van Duijkeren, Niels, Spies, Markus, Kesper, Patrick, Hoppe, Sabrina, Ziesche, Hanna, Bürger, Mathias, Arras, Kai O.
Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and
Externí odkaz:
http://arxiv.org/abs/2304.10595
Autor:
Graf, Christian, Adrian, David B., Weil, Joshua, Gabriel, Miroslav, Schillinger, Philipp, Spies, Markus, Neumann, Heiko, Kupcsik, Andras
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex datasets, such as registered RGBD sequences, we train on an unordered se
Externí odkaz:
http://arxiv.org/abs/2209.05213
We propose a framework for robust and efficient training of Dense Object Nets (DON) with a focus on multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multi
Externí odkaz:
http://arxiv.org/abs/2206.12145
Autor:
F. Kupcsik
Publikováno v:
European Psychiatry, Vol 67, Pp S827-S828 (2024)
Introduction The purpose of my presentation is to introduce the Hungarian Association of Psychiatric Trainees (HAPT), - our NAT - to you, which includes residents and young specialists within five years of training. Objectives Currently we have 108
Externí odkaz:
https://doaj.org/article/0922df44e6604f87b7771d717286eb6b
Autor:
Le, An T., Guo, Meng, van Duijkeren, Niels, Rozo, Leonel, Krug, Robert, Kupcsik, Andras G., Buerger, Mathias
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose
Externí odkaz:
http://arxiv.org/abs/2109.04222
Autor:
Kupcsik, Andras, Spies, Markus, Klein, Alexander, Todescato, Marco, Waniek, Nicolai, Schillinger, Philipp, Buerger, Mathias
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning,
Externí odkaz:
http://arxiv.org/abs/2102.08096
Autor:
Rozo, Leonel, Guo, Meng, Kupcsik, Andras G., Todescato, Marco, Schillinger, Philipp, Giftthaler, Markus, Ochs, Matthias, Spies, Markus, Waniek, Nicolai, Kesper, Patrick, Büerger, Mathias
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation ta
Externí odkaz:
http://arxiv.org/abs/2008.10471
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitl
Externí odkaz:
http://arxiv.org/abs/1904.11761
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