IROSA: Interactive Robot Skill Adaptation using Natural Language
Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that enables open-vocabulary skill adaptation through a tool-based architecture, maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre-trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine-t...
| Authors | Markus Knauer, Samuel Bustamante, Thomas Eiband et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 March 2026 |
| Last updated at source | 16 April 2026 |
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