3 papers
cs.RO2026
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…
cs.RO2026
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…
cs.RO2026
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,…