2 citations · 2 across the 4 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…
CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
Markus Knauer, Valentin Gieraths, Tai Mai +4
Enabling robots to understand and execute tasks from natural language commands while maintaining data efficiency remains challenging. Foundation models such as vision-language-acti…
IROSA: Interactive Robot Skill Adaptation using Natural Language
Markus Knauer, Samuel Bustamante, Thomas Eiband +3
Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited dat…
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…
Interactive incremental learning of generalizable skills with local trajectory modulation
Markus Knauer, Alin Albu-Schäffer, Freek Stulp +1
The problem of generalization in learning from demonstration (LfD) has received considerable attention over the years, particularly within the context of movement primitives, where…