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 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.
PIC4rl-gym: a ROS2 Modular Framework for Robots Autonomous Navigation with Deep Reinforcement Learning / Martini, Mauro; Eirale, Andrea; Cerrato, Simone; Chiaberge, Marcello. - ELETTRONICO. - (2023), pp. 198-202. (Intervento presentato al convegno 2023 3rd International Conference on Computer, Control and Robotics (ICCCR) tenutosi a Shanghai, China nel 24-26 March 2023) [10.1109/ICCCR56747.2023.10193996].
PIC4rl-gym: a ROS2 Modular Framework for Robots Autonomous Navigation with Deep Reinforcement Learning
Martini, Mauro;Eirale, Andrea;Cerrato, Simone;Chiaberge, Marcello
2023
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 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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2980925