ROS2Learn: a reinforcement learning framework for ROS 2

Yue Leire Erro Nuin; Nestor Gonzalez Lopez; Elias Barba Moral; Lander Usategui San Juan; Alejandro Solano Rueda; Víctor Mayoral Vilches; Risto Kojcev

arXiv · 1903.06282

ROS2Learn: a reinforcement learning framework for ROS 2

Yue Leire Erro Nuin·Nestor Gonzalez Lopez·Elias Barba Moral·Lander Usategui San Juan·Alejandro Solano Rueda·Víctor Mayoral Vilches·Risto Kojcev

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Abstract

We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics to train a robot directly from joint states, using traditional robotic tools. We use an state-of-the-art implementation of the Proximal Policy Optimization, Trust Region Policy Optimization and Actor-Critic Kronecker-Factored Trust Region algorithms to learn policies in four different Modular Articulated Robotic Arm (MARA) environments. We support this process using a framework that communicates with typical tools used in robotics, such as Gazebo and Robot Operating System 2 (ROS 2). We evaluate several algorithms in modular robots with an empirical study in simulation.

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Yue Leire Erro Nuin, Nestor Gonzalez Lopez, Elias Barba Moral, Lander Usategui San Juan, Alejandro Solano Rueda, Víctor Mayoral Vilches, Risto Kojcev. 2019-03-14. ROS2Learn: a reinforcement learning framework for ROS 2. https://arxiv.org/abs/1903.06282

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