RobotIQ: Empowering Mobile Robots with Human-Level Planning for Real-World Execution

Emmanuel K. Raptis; Athanasios Ch. Kapoutsis; Elias B. Kosmatopoulos

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arXiv · 2502.12862

RobotIQ: Empowering Mobile Robots with Human-Level Planning for Real-World Execution

Emmanuel K. Raptis·Athanasios Ch. Kapoutsis·Elias B. Kosmatopoulos

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Abstract

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.

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Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos. 2025-02-18. RobotIQ: Empowering Mobile Robots with Human-Level Planning for Real-World Execution. https://arxiv.org/abs/2502.12862

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