← 研踪 PaperTrailarXiv:2608.00635 · v2
FlowPilot: Real-Time World-Action Modeling for Agile UAV Navigation
首次提交 2026-08-01 20:43
版本更新 2026-11-08 14:44
雷达观测 2026-11-10 12:16
摘要
We present FlowPilot, the first world-action model for real-time onboard UAV navigation from depth. Unlike map-then-optimize pipelines that require local reconstruction or end-to-end policies that lack explicit scene prediction, FlowPilot jointly denoises future depth observations and executable trajectories with flow matching. A dual-stream mixture-of-transformers couples video and action experts through shared attention, allowing future-scene prediction and trajectory generation to inform each other. At deployment, the model runs action-centrically and outputs only a trajectory. To generate smooth, state-consistent references, actions are parameterized as degree-7 Bernstein polynomials: the current state constrains the initial control points, and the network predicts five free control points, yielding C^2-continuous references with closed-form velocity, acceleration and jerk. FlowPilot is trained on a three-level depth pyramid spanning high-throughput simulation, photorealistic simulation, and real onboard data. In closed-loop simulation, it outperforms learning- and optimization-based baselines under increasing clutter and commanded speeds up to 10 m/s. On a physical quadrotor, FlowPilot achieves a mean inference latency of 10.2 ms on a Jetson Orin NX and reaches a peak speed of 10 m/s during outdoor flight using only onboard sensing and computation.
作者备注：8 pages, 8 figures, 4 tables. Accepted for publication in IEEE Robotics and Automation Letters (RA-L)
观测到的版本
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v2 · 2026-11-08 14:44 · 最新观测版本
FlowPilot: Real-Time World-Action Modeling for Agile UAV Navigation
We present FlowPilot, the first world-action model for real-time onboard UAV navigation from depth. Unlike map-then-optimize pipelines that require local reconstruction or end-to-end policies that lack explicit scene prediction, FlowPilot jointly denoises future depth observations and executable trajectories with flow matching. A dual-stream mixture-of-transformers couples video and action experts through shared attention, allowing future-scene prediction and trajectory generation to inform each other. At deployment, the model runs action-centrically and outputs only a trajectory. To generate smooth, state-consistent references, actions are parameterized as degree-7 Bernstein polynomials: the current state constrains the initial control points, and the network predicts five free control points, yielding C^2-continuous references with closed-form velocity, acceleration and jerk. FlowPilot is trained on a three-level depth pyramid spanning high-throughput simulation, photorealistic simulation, and real onboard data. In closed-loop simulation, it outperforms learning- and optimization-based baselines under increasing clutter and commanded speeds up to 10 m/s. On a physical quadrotor, FlowPilot achieves a mean inference latency of 10.2 ms on a Jetson Orin NX and reaches a peak speed of 10 m/s during outdoor flight using only onboard sensing and computation.
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