103 citations · 103 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Are Foundation Models the Route to Full-Stack Transfer in Robotics?
Freek Stulp, Samuel Bustamante, João Silvério +3
In humans and robots alike, transfer learning occurs at different levels of abstraction, from high-level linguistic transfer to low-level transfer of motor skills. In this article,…
Model Reconciliation through Explainability and Collaborative Recovery in Assistive Robotics
Britt Besch, Tai Mai, Jeremias Thun +4
Whenever humans and robots work together, it is essential that unexpected robot behavior can be explained to the user. Especially in applications such as shared control the user an…