arplaboratory/learning-to-fly

End-to-end optimal quadcopter control through Reinforcement Learning

PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control

A Foundation Policy for Quadrotor Control

Scalable drone simulation using JAX.

Genesis Reinforcement Learning Environments for Drones

Joinied Positioning and Control Model Based on Factor Graph Optimization (FGO)

A complete, hardware-ready Python package for Koopman-based Linear Model Predictive Control (LMPC), delivering real-time trajectory tracking for quadrotors using analytical Koopman lifting (no training data required)

AM-Bench: a modular simulation suite and benchmark for aerial manipulation policy learning (CoRL 2026)

Code for quadrotor control