IROSA: 基于自然语言的交互机器人技能适应
IROSA: Interactive Robot Skill Adaptation using Natural Language
AI总结：
本文提出IROSA框架，利用预训练大语言模型实现开放词汇技能适应，通过工具架构在语言模型与机器人硬件间保持抽象层，无需微调即可通过自然语言指令调整机器人技能。
摘要：
基础模型在多样领域展现出卓越能力，而模仿学习为从有限数据中适应机器人技能提供了系统方法。结合这两种方法对机器人应用具有巨大潜力，但这一结合在工业部署中受到限制。本文提出一种新型框架，通过基于工具的架构实现开放词汇技能适应，在语言模型与机器人硬件之间保持保护性抽象层。我们的方法利用预训练的大语言模型选择并参数化特定工具，以适应机器人技能，而无需微调或直接模型-机器人交互。我们在一个7自由度扭矩控制机器人上演示了该框架，执行工业轴承环插入任务，通过自然语言指令实现速度调整、轨迹修正和障碍物避让，同时保持安全、透明和可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
