30 citations · 65 across the 4 of their papers we have counts for
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cs.RO2026
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…
cs.RO2026
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…