IROSA: Interactive Robot Skill Adaptation using Natural Language
This work addresses the challenge of flexible and safe robot skill adaptation in industrial settings, though it appears incremental by combining existing foundation models and imitation learning methods.
The paper tackles the problem of adapting robot skills for industrial tasks using natural language, presenting a framework that successfully enables a 7-DoF robot to perform bearing ring insertion with commands for speed adjustment, trajectory correction, and obstacle avoidance while ensuring safety and interpretability.
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.