IROSA: Interactive Robot Skill Adaptation using Natural Language
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910134155870208 |
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| author | Knauer, Markus Bustamante, Samuel Eiband, Thomas Albu-Schäffer, Alin Stulp, Freek Silvério, João |
| author_facet | Knauer, Markus Bustamante, Samuel Eiband, Thomas Albu-Schäffer, Alin Stulp, Freek Silvério, João |
| contents | 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-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03897 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IROSA: Interactive Robot Skill Adaptation using Natural Language Knauer, Markus Bustamante, Samuel Eiband, Thomas Albu-Schäffer, Alin Stulp, Freek Silvério, João Robotics Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning 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-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability. |
| title | IROSA: Interactive Robot Skill Adaptation using Natural Language |
| topic | Robotics Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2603.03897 |