让无人机在延迟中保持敏捷，通过异步学习实现高频控制
Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
- 分离感知与控制，用最新IMU数据实时响应
- 100Hz控制频率，真实场景下稳定穿越复杂环境
- 适合需要高速反应的无人机导航研究者
复杂环境中自主飞行器的鲁棒自主导航至关重要。然而，现代端到端导航面临核心挑战：高速飞行所需的高频控制周期与受限于传感器更新率和高计算成本的低频感知流不匹配，导致传统同步模型只能采用过低的控制频率。为此，我们提出一种异步强化学习框架，解耦感知与控制，使高频率策略基于最新惯性测量单元（IMU）状态即时响应，同时异步融合感知特征。为应对数据滞后问题，引入理论支撑的时序编码模块（TEM），显式将感知延迟纳入策略条件，辅以两阶段课程训练确保稳定高效训练。大规模仿真验证后，该方法成功实现零样本仿真到现实的迁移，在机载NUC上实现100~Hz控制频率，展现出在真实复杂环境中稳健且敏捷的导航能力。源代码将开源供社区参考。
原文摘要 · Abstract (English)
Robust autonomous navigation for Autonomous Aerial Vehicles (AAVs) in complex environments is a critical capability. However, modern end-to-end navigation faces a key challenge: the high-frequency control loop needed for agile flight conflicts with low-frequency perception streams, which are limited by sensor update rates and significant computational cost. This mismatch forces conventional synchronous models into undesirably low control rates. To resolve this, we propose an asynchronous reinforcement learning framework that decouples perception and control, enabling a high-frequency policy to act on the latest IMU state for immediate reactivity, while incorporating perception features asynchronously. To manage the resulting data staleness, we introduce a theoretically-grounded Temporal Encoding Module (TEM) that explicitly conditions the policy on perception delays, a strategy complemented by a two-stage curriculum to ensure stable and efficient training. Validated in extensive simulations, our method was successfully deployed in zero-shot sim-to-real transfer on an onboard NUC, where it sustains a 100~Hz control rate and demonstrates robust, agile navigation in cluttered real-world environments. Our source code will be released for community reference.
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