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：基础模型已在不同领域展现出令人瞩目的能力，而模仿学习则提供了基于有限数据实现机器人技能自适应的原理性方法。将这两种方法结合有望直接应用于机器人领域，然而这一结合尚未获得充分关注，特别是在工业部署层面。我们提出一种新颖框架，通过基于工具的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构建保护性抽象层。该方法利用预训练大型语言模型选择并参数化特定工具以适配机器人技能，无需微调或模型与机器人直接交互。我们在一个七自由度力矩控制机器人执行工业轴承嵌件装配任务中验证该框架，展示了通过自然语言命令实现速度调整、轨迹修正与障碍规避的技能自适应能力，同时确保安全性、透明度与可解释性。