3 citations · 3 across the 7 of their papers we have counts for
7 papers
Cycle-Correspondence Loss: Learning Dense View-Invariant Visual Features from Unlabeled and Unordered RGB Images
David B. Adrian, Andras Gabor Kupcsik, Markus Spies +1
Robot manipulation relying on learned object-centric descriptors became popular in recent years. Visual descriptors can easily describe manipulation task objectives, they can be le…
Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking
Yushi Liu, Alexander Qualmann, Zehao Yu +5
Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the predictio…
The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing
Leonel Rozo, Andras G. Kupcsik, Philipp Schillinger +9
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…
Learning Dense Visual Descriptors using Image Augmentations for Robot Manipulation Tasks
Christian Graf, David B. Adrian, Joshua Weil +5
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex da…
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation
David B. Adrian, Andras Gabor Kupcsik, Markus Spies +1
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 d…
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
Andras Kupcsik, Markus Spies, Alexander Klein +4
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 suit…
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Leonel Rozo, Meng Guo, Andras G. Kupcsik +8
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 works…