PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning

TL;DR

This paper introduces PIC4rl-gym, a modular ROS2-based framework combining Gazebo and Deep Reinforcement Learning to facilitate autonomous navigation research with customizable simulation environments.

Contribution

The paper presents a novel modular framework that integrates ROS2, Gazebo, and DRL for autonomous navigation, enabling easy customization and comprehensive benchmarking.

Findings

Effective training and testing of DRL agents in various navigation scenarios

Flexible customization of simulation platforms, sensors, and models

Benchmarking results demonstrating the framework's capabilities

Abstract

Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep Reinforcement Learning (DRL). The paper describes the whole structure of the PIC4rl-gym, which fully integrates DRL agent's training and testing in several indoor and outdoor navigation scenarios and tasks. A modular approach is adopted to easily customize the simulation by selecting new platforms, sensors, or models. We demonstrate the potential of our novel gym by benchmarking the resulting policies, trained for different navigation tasks, with a complete set of metrics.