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

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

TL;DR

RobotIQ is a framework that empowers mobile robots with human-level planning capabilities, enabling seamless communication via natural language instructions through any Large Language Model, designed in the ROS architecture and aims to bridge the gap between humans and robots.

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.

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

TL;DR

RobotIQ is a framework that empowers mobile robots with human-level planning capabilities, enabling seamless communication via natural language instructions through any Large Language Model, designed in the ROS architecture and aims to bridge the gap between humans and robots.

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.

Paper Structure

This paper contains 26 sections, 13 equations, 12 figures.

Table of Contents

  1. Introduction
  2. Paper Outline
  3. Related Work
  4. Contributions
  5. Robot Library Suite
  6. Assumptions
  7. Approach & Rationale
  8. Reinforcement Learning-Driven Navigation
  9. Problem formulation
  10. State Space
  11. Action Space
  12. Reward Function
  13. Performance Evaluation
  14. Augmented Reality-Driven Object Localization
  15. Pick & Place
  16. ...and 11 more sections

Figures (12)

  • Figure 1: RobotIQ: The overall framework of our approach.
  • Figure 2: Definitions of the state variables in an overhead view of the robot.
  • Figure 3: Overview of the experimental architecture for reinforcement learning, depicting the alignment between the environment and its associated state-action pairs.
  • Figure 4: Learning curves for the simulated environment with discrete & continuous action space.
  • Figure 5: Illustration of move$\_$group node communicating with ROS topics, facilitating information exchange on joint forces, torques, and poses.
  • ...and 7 more figures