FRobs_RL: A flexible robotics reinforcement learning library

Fajardo, Jose Manuel, González Roldan, Felipe, Realpe, Sebastian, Hernandez, Juan D. ORCID: https://orcid.org/0000-0002-9593-6789, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 and Cardenas, Pedro-F 2022. FRobs_RL: A flexible robotics reinforcement learning library. Presented at: IEEE International Conference on Automation Science and Engineering (CASE), Mexico City, 20 - 24 August 2022. 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 10.1109/CASE49997.2022.9926586

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Abstract

Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications. FRobs_RL is a Python library that aims to facilitate the implementation, testing, and deployment of RL algorithms in intelligent robotic applications using robot operating system (ROS), Gazebo, and OpenAI Gym. FRobs_RL provides an Application Programming Interface (API) to simplify the creation of RL environments, where users can import a wide variety of robot models as well as different simulated environments. With the FRobs_RL library, users do not need to be experts in ROS, Gym, or Gazebo to create a realistic RL application. Using the library, we created and tested two environments containing common robotic tasks; one is a reacher task using a robotic manipulator, and the other is a mapless navigation task using a mobile robot. The library is available in GitHub 1 .

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-6654-9042-9
Date of First Compliant Deposit:	26 June 2022
Date of Acceptance:	22 May 2022
Last Modified:	28 Nov 2022 16:42
URI:	https://orca.cardiff.ac.uk/id/eprint/150796

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