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。将这两种方法相结合有望直接应用于机器人领域，但这一结合目前尚未得到充分关注，尤其在工业部署方面。本文提出了一种新颖框架，通过基于工具的系统架构实现开放词汇技能自适应，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，无需进行微调或建立直接的模型-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，通过自然语言指令成功实现了速度调节、轨迹修正和避障等技能自适应，同时保持了系统的安全性、透明度和可解释性。