Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics

arXiv:2607.25985v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs), particularly quadcopters, present unique challenges for autonomous control due to their underactuated dynamics: only four available control inputs must govern six degrees of freedom. This paper investigates a physics-aware, end-to-end deep reinforcement learning (DRL) approach that acts directly on low-level body inputs, total thrust and body torques $(T, \tau_x, \tau_y, \tau_z)$, and closes the loop through a high-...