gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo
arXiv:1903.06278
Abstract
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. The content discusses the new ROS 2 based software architecture and summarizes the results obtained using Proximal Policy Optimization (PPO). Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions. We have evaluated environments with different levels of complexity of the Modular Articulated Robotic Arm (MARA), reaching accuracies in the millimeter scale. The converged results show the feasibility and usefulness of the gym-gazebo 2 toolkit, its potential and applicability in industrial use cases, using modular robots.
References in corpus (9)
- A Critical Review of Recurrent Neural Networks for Sequence Learning
- Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
- Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
- Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo
- ROS2Learn: a reinforcement learning framework for ROS 2
- Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder
- Hierarchical Learning for Modular Robots
- Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo
- Towards self-adaptable robots: from programming to training machines