This paper introduces RobotIQ, a framework that empowers mobile robots with human-level planning capabilities, enabling seamless communication via natural language instructions through any Large Language Model. The proposed framework is designed in the ROS architecture and aims to bridge the gap between humans and robots, enabling robots to comprehend and execute user-expressed text or voice commands. Our research encompasses a wide spectrum of robotic tasks, ranging from fundamental logical, mathematical, and learning reasoning for transferring knowledge in domains like navigation, manipulation, and object localization, enabling the application of learned behaviors from simulated environments to real-world operations. All encapsulated within a modular crafted robot library suite of API-wise control functions, RobotIQ offers a fully functional AI-ROS-based toolset that allows researchers to design and develop their own robotic actions tailored to specific applications and robot configurations. The effectiveness of the proposed system was tested and validated both in simulated and real-world experiments focusing on a home service scenario that included an assistive application designed for elderly people. RobotIQ with an open-source, easy-to-use, and adaptable robotic library suite for any robot can be found at https://github.com/emmarapt/RobotIQ.

翻译：本文介绍RobotIQ框架，该框架赋予移动机器人人类级规划能力，使其能够通过任何大型语言模型以自然语言指令进行无缝通信。所提出的框架基于ROS架构设计，旨在弥合人类与机器人之间的鸿沟，使机器人能够理解并执行用户通过文本或语音表达的指令。我们的研究涵盖广泛的机器人任务范围，包括用于在导航、操控和物体定位等领域传递知识的基础逻辑、数学和学习推理，从而支持将模拟环境中习得的行为应用于真实世界操作。所有功能均封装于模块化构建的机器人库套件中，该套件提供API级控制函数，RobotIQ由此提供一套功能完整的基于AI-ROS的工具集，使研究人员能够针对特定应用和机器人配置设计与开发定制化的机器人行为。所提出系统的有效性在模拟和真实世界实验中得到了测试与验证，实验聚焦于家庭服务场景，其中包含专为老年人设计的辅助应用。RobotIQ作为开源、易用且可适配于任意机器人的机器人库套件，可通过https://github.com/emmarapt/RobotIQ获取。