4 citations · 8 across the 8 of their papers we have counts for
9 papers
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…
Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
An T. Le, Meng Guo, Niels van Duijkeren +4
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes an…
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…
Factored Contextual Policy Search with Bayesian Optimization
Robert Pinsler, Peter Karkus, Andras Kupcsik +2
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…
Factored Contextual Policy Search with Bayesian Optimization
Peter Karkus, Andras Kupcsik, David Hsu +1
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different "contexts". Bayesian optimizatio…
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
Andras Kupcsik, David Hsu, Wee Sun Lee
Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing wo…