19 citations · 19 across the 3 of their papers we have counts for
5 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…
Passive Variable Impedance For Shared Control
Maximilian Mühlbauer, Nepomuk Werner, Ribin Balachandran +4
Shared Control methods often use impedance control to track target poses in a robotic manipulator. The guidance behavior of such controllers is shaped by the used stiffness gains,…
MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
Markus Knauer, Edoardo Fiorini, Maximilian Mühlbauer +10
Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefi…
AI-enabled Cyber-Physical In-Orbit Factory -- AI approaches based on digital twin technology for robotic small satellite production
Florian Leutert, David Bohlig, Florian Kempf +12
With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-calle…