Author

Maximilian Mühlbauer

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Preprint Oct 2026

Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors

When learning probabilistic policies from human demonstrations, data-efficient learning and fast adaptations to new scenarios are key requirements. One popular way to achieve intuitive and reliable adaptations is through non-parametric, typically kernel-based, methods. However, existing solutions either fail to account...

Maximilian Mühlbauer, Arne Sachtler, Markus Knauer et al. · 0 citations

Preprint Sep 2026

Learning Geometry-Aware Virtual Fixtures From Sparse Demonstrations

In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (LfD) approaches may not be feasible. To still give operators the ability to intuitively create trajectories as Virtual Fixtures (VFs), we propose to leverage a motion pr...

Maximilian Mühlbauer, Marcella Piacentino, R. Mancino et al. · 0 citations