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.

翻译：基础模型已在多个领域展现出卓越能力，而模仿学习则为基于有限数据的机器人技能自适应提供了理论方法。结合这两种方法为机器人技术的直接应用带来了巨大潜力，但该融合方向尚未得到充分关注，尤其在工业部署领域。我们提出了一种新颖框架，通过基于工具的架构实现开放词汇的技能自适应，在语言模型与机器人硬件之间保持保护性抽象层。该方法利用预训练大语言模型选择和参数化特定工具以适配机器人技能，无需微调或直接的模型-机器人交互。我们在执行工业轴承环装配任务的7自由度扭矩控制机器人上验证了该框架，展示了通过自然语言指令（包括速度调整、轨迹修正和避障）成功实现技能自适应，同时保持了安全性、透明度和可解释性。