# Wind Disturbance Estimation

The paper proposes WAND, a reinforcement learning framework for quadrotor navigation in dense obstacle fields under time-varying wind: a Temporal Convolutional Network estimates wind-induced disturbance acceleration from proprioceptive histories, the estimate is injected into the policy through a zero-initialized residual module (WindAdapter) and reused for low-level feedforward compensation, improving observed success rate by 8.3 percentage points on average over feedforward compensation alone across 12 wind-disturbed simulation settings and succeeding in 18 of 20 indoor fan-induced flight trials.

The paper proposes WAND, a reinforcement learning framework for quadrotor navigation in dense obstacle fields under time-varying wind: a Temporal Convolutional Network estimates wind-induced disturbance acceleration from proprioceptive histories, the estimate is injected into the policy through a zero-initialized residual module (WindAdapter) and reused for low-level feedforward compensation, improving observed success rate by 8.3 percentage points on average over feedforward compensation alone across 12 wind-disturbed simulation settings and succeeding in 18 of 20 indoor fan-induced flight trials.