GymFG: A Framework with a Gym Interface for FlightGear
This provides a tool for researchers and developers to prototype and evaluate autonomous aerial agents, addressing trust issues in autonomy for complex flight tasks, though it is incremental as it builds on existing simulators and learning frameworks.
The authors tackled the lack of an open-source platform for developing autonomous flight systems by creating GymFG, a framework that integrates a high-fidelity flight simulator with an agent learning framework, and demonstrated its use by training an autonomous aerial agent using Imitation Learning.
Over the past decades, progress in deployable autonomous flight systems has slowly stagnated. This is reflected in today's production air-crafts, where pilots only enable simple physics-based systems such as autopilot for takeoff, landing, navigation, and terrain/traffic avoidance. Evidently, autonomy has not gained the trust of the community where higher problem complexity and cognitive workload are required. To address trust, we must revisit the process for developing autonomous capabilities: modeling and simulation. Given the prohibitive costs for live tests, we need to prototype and evaluate autonomous aerial agents in a high fidelity flight simulator with autonomous learning capabilities applicable to flight systems: such a open-source development platform is not available. As a result, we have developed GymFG: GymFG couples and extends a high fidelity, open-source flight simulator and a robust agent learning framework to facilitate learning of more complex tasks. Furthermore, we have demonstrated the use of GymFG to train an autonomous aerial agent using Imitation Learning. With GymFG, we can now deploy innovative ideas to address complex problems and build the trust necessary to move prototypes to the real-world.