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

Autonomous navigation algorithms aim at providing mobile robots with efficient planning and control policies to go through cluttered and dynamic environments. Advanced autonomous navigation systems have been explored to improve planners' and controllers' robustness, reliability, and computational efficiency in real-world applications. In the last decade, learning methods have seen a tremendous success among robotics researchers, motivating an increasing collection of innovative works which adopt Deep Reinforcement Learning (DRL) for general autonomous navigation [1], socially aware path planning [2], and agile aerial vehicles autopilot [3]. Besides the most common paradigm of sensorimotor agents or local planners, learning agents can be successfully mixed up in alternative ways with the navigation system. Recent works proposed hybrid solutions to optimize classic planners like the Dynamic Window Approach (DWA) [4]. Moreover, [5, 6] recently showed the effectiveness of the planner's parameters learning approach compared to end-to-end policy learning, resulting in an adaptive optimized planner.