30 citations · 65 across the 4 of their papers we have counts for
6 papers
Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors
Maximilian Mühlbauer, Arne Sachtler, Markus Knauer +4
When learning probabilistic policies from human demonstrations, data-efficient learning and fast adaptations to new scenarios are key requirements. One popular way to achieve intui…
Learning Geometry-Aware Virtual Fixtures From Sparse Demonstrations
Maximilian Mühlbauer, Marcella Piacentino, Raffaella Mancino +10
In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (LfD) approaches may not be fe…
A roadmap for AI in robotics
Aude Billard, Alin Albu-Schaeffer, Michael Beetz +8
AI technologies, including deep learning, large-language models have gone from one breakthrough to the other. As a result, we are witnessing growing excitement in robotics at the p…
Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments
Michael G. Adam, Sebastian Eger, Martin Piccolrovazzi +10
As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the appli…
Model Predictive Control for Flexible Joint Robots
Maged Iskandar, Christiaan van Ommeren, Xuwei Wu +2
Modern Lightweight robots are constructed to be collaborative, which often results in a low structural stiffness compared to conventional rigid robots. Therefore, the controller mu…
Redundancy Resolution at Position Level
Alin Albu-Schäffer, Arne Sachtler
Increasing the degrees of freedom of robotic systems makes them more versatile and flexible. This usually renders the system kinematically redundant: the main manipulation or inter…