gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo

1 code implementation • 14 Mar 2019 • Nestor Gonzalez Lopez, Yue Leire Erro Nuin, Elias Barba Moral, Lander Usategui San Juan, Alejandro Solano Rueda, Víctor Mayoral Vilches, Risto Kojcev

This paper presents an upgraded, real world application oriented version of gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement Learning (RL) toolkit, which complies with OpenAI Gym.

Benchmarking OpenAI Gym +3

ROS2Learn: a reinforcement learning framework for ROS 2

2 code implementations • 14 Mar 2019 • Yue Leire Erro Nuin, Nestor Gonzalez Lopez, Elias Barba Moral, Lander Usategui San Juan, Alejandro Solano Rueda, Víctor Mayoral Vilches, Risto Kojcev

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.

reinforcement-learning Reinforcement Learning (RL)

Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo

no code implementations • 30 Aug 2018 • Víctor Mayoral Vilches, Alejandro Hernández Cordero, Asier Bilbao Calvo, Irati Zamalloa Ugarte, Risto Kojcev

We unveil that, for simple tasks, simple 3DoF robots require more than 140 attempts to learn.

An information model for modular robots: the Hardware Robot Information Model (HRIM)

1 code implementation • 5 Feb 2018 • Irati Zamalloa, Iñigo Muguruza, Alejandro Hernández, Risto Kojcev, Víctor Mayoral

Opposite to this, an horizontal integration would result in a rapid development of cost-effective mass-market products with an additional consumer empowerment.

Robotics