aiqbench analysis
The paper addresses a practical limitation of vision-language-action models on drones: onboard inference delay makes predicted trajectories fall out of step with the aircraft's motion. Its approach combines trajectory fusion with execution-aware reinforcement learning (GRPO), reporting a 76.56% success rate on the UAV-Flow test set. These are simulation-benchmark results with code not yet released, so readers should watch for independent replication and real-world flight data.
AeroDASH: Asynchronous Vision-Language-Action Control for Continuous UAV Flight
arXiv:2610.25407v1 Announce Type: new Abstract: 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.
Abstract: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.
Robotics (cs.RO)
arXiv:2610.25407 [cs.RO]
(or arXiv:2610.25407v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.25407 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 257 words