4 papers
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…
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…
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…
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…