AeroDASH: Asynchronous Vision-Language-Action Control for Continuous UAV Flight

Overview & Synopsis

Onboard inference latency creates a mismatch between the trajectories predicted by vision-language-action (VLA) policies and the ongoing motion of unmanned aerial vehicles (UAVs). We present AeroDASH, an asynchronous VLA framework for continuous language-conditioned flight. A B-spline policy is initialized through supervised fine-tuning on expert demonstrations, providing continuous trajectory references in position and yaw. During execution, trajectory fusion removes the expired prediction prefix and connects the remaining segment to the active reference with derivative continuity while limiting trajectory deformation. To account for deviations induced by asynchronous execution, execution-aware group relative policy optimization (GRPO) further adapts the policy using trajectory-level rewards. On the UAV-Flow test set, AeroDASH achieves a success rate of 76.56%, exceeding OpenVLA-UAV and AeroDASH w/o GRPO by 38.1 and 20.1 percentage points, respectively. Execution experiments show that AeroDASH sustains motion during inference and reduces acceleration, jerk, and speed fluctuations relative to asynchronous tracking of waypoint sequences. We further demonstrate language-conditioned flight with onboard inference and asynchronous trajectory execution. The code will be released upon acceptance.