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An Educational Kit for Simulated Robot Learning in ROS 2

  • Conference paper
  • First Online:
Robot 2023: Sixth Iberian Robotics Conference (ROBOT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 978))

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Abstract

Robot Learning is one of the most important areas in Robotics and its relevance has only been increasing. The Robot Operating System (ROS) has been one of the most used architectures in Robotics but learning it is not a simple task. Additionally, ROS 1 is reaching its end-of-life and a lot of users are yet to make the transition to ROS 2. Reinforcement Learning (RL) and Robotics are rarely taught together, creating greater demand for tools to teach all these components. This paper aims to develop a learning kit that can be used to teach Robot Learning to students with different levels of expertise in Robotics. This kit works with the Flatland simulator using open-source free software, namely the OpenAI Gym and Stable-Baselines3 packages, and contains tutorials that introduce the user to the simulation environment as well as how to use RL to train the robot to perform different tasks. User tests were conducted to better understand how the kit performs, showing very positive feedback, with most participants agreeing that the kit provided a productive learning experience.

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Notes

  1. 1.

    https://github.com/avidbots/flatland.

  2. 2.

    https://github.com/GoncaloLeao/Scientific-Research.

  3. 3.

    http://design.ros2.org/articles/changes.html.

  4. 4.

    http://wiki.ros.org/Documentation.

  5. 5.

    https://docs.ros.org/en/foxy/index.html.

  6. 6.

    https://github.com/ros2/ros1_bridge.

  7. 7.

    https://github.com/duckietown/gym-duckietown.

  8. 8.

    https://github.com/JoaoCostaIFG/flatland.

  9. 9.

    https://www.gymlibrary.dev/.

  10. 10.

    https://github.com/FilipeAlmeidaFEUP/ros2_teleopkeys_tutorial.

  11. 11.

    https://github.com/FilipeAlmeidaFEUP/ros2_teleopkeys_publisher.

  12. 12.

    https://github.com/FilipeAlmeidaFEUP/ros2_flatland_rl_tutorial.

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Acknowledgments

This work is co-financed by Component 5 - Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021 – 2026, within project Hi_reV, with reference 64.

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Porto (FEUP), 4200-465, Porto, Portugal

    Filipe Almeida, Gonçalo Leão & Armando Sousa

  2. Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465, Porto, Portugal

    Gonçalo Leão & Armando Sousa

Authors
  1. Filipe Almeida
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  2. Gonçalo Leão
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  3. Armando Sousa
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Corresponding author

Correspondence to Gonçalo Leão .

Editor information

Editors and Affiliations

  1. Dep. de Eng. Electrotecnica e de Computadores, University of Coimbra, Coimbra, Portugal

    Lino Marques

  2. Department of Electrónica Industrial, University of Minho, Escola de Engenharia, Guimarães, Portugal

    Cristina Santos

  3. Department of Electrical Engineering, ESTIG, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  4. Centro Universitario de la Defensa, Zaragoza, Spain

    Danilo Tardioli

  5. Centre for Automation and Robotics UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain

    Manuel Ferre

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Almeida, F., Leão, G., Sousa, A. (2024). An Educational Kit for Simulated Robot Learning in ROS 2. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_42

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