I’ve open-sourced GS-DroneGym, a drone-first research stack for vision-language-action work. Main idea: instead of only using synthetic assets, it can render observations from 3D Gaussian Splatting scenes, so you can prototype aerial waypoint policies in environments much closer to real visual conditions. Current features:
- 6-DOF quadrotor dynamics
- waypoint controller for [x, y, z, yaw]
- gsplat renderer with CPU fallback
- navigation tasks: PointNav, ObjectNav, ObstacleSlalom, DynamicFollow, NarrowCorridor
- live viewer with RGB / depth / top-down trajectory
- shared trajectory schema + dataset/eval tooling
- adapters for GS-DroneGym, LIBERO, and LeRobot-format datasets https://github.com/09Catho/gs-dronegym Please star the repo if you find ut useful I’d especially appreciate feedback on:
- sim-to-real usefulness
- dataset generation for aerial VLA training
- benchmark design for drone navigation submitted by /u/Financial_World_9730
Originally posted by u/Financial_World_9730 on r/ArtificialInteligence
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