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

2025-02-18

AI Summary

To address the limited natural language understanding and task planning capabilities of mobile robots, this paper introduces RobotIQ—a novel modular AI-ROS co-planning framework driven by large language models (LLMs). RobotIQ employs an LLM as a high-level task interpreter and decomposer, interfacing with ROS-based low-level control via standardized APIs, and integrates logical reasoning, mathematical solving, and learning-based execution modules to enable end-to-end mapping from natural language instructions (text or speech) to embodied actions—including navigation, manipulation, and object localization. Its key innovation lies in enabling cross-simulation-to-reality knowledge transfer. The framework is validated in home-service scenarios, including elder assistance tasks. Designed as open-source, RobotIQ is compatible with any ROS-compatible robot platform, significantly enhancing the naturalness of human–robot interaction and generalization to open-domain tasks.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Enhance mobile robots with human-level planning.
• Enable robots to execute natural language commands.
• Bridge gap between humans and robotic systems.

Innovation

• Human-level planning capabilities
• Natural language communication integration
• Modular ROS-based API library