让无人机通过复杂不规则缝隙，直接从深度图生成控制指令。
Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
- 用可微分仿真直接从深度图映射控制指令，端到端导航。
- 在模拟和真实环境中均成功穿越连续不规则缝隙，表现稳定。
- 适合需要自主穿行复杂环境的无人机任务，如搜救与巡检。
在巡检、搜救和灾后响应等应用中，自主无人机穿越狭窄且不规则缝隙是一项关键能力。传统规划与控制方法依赖显式缝隙提取与测量，而近期端到端方法常假设缝隙为规则形状，导致泛化能力差、实用性受限。本文提出一种全视觉、端到端框架，将深度图像直接映射为控制指令，使无人机可在未见过的环境中穿越复杂缝隙。该框架基于特殊欧几里得群 SE(3)，耦合位置与姿态，结合可微分仿真、停梯度操作符与双模初始化分布，实现连续缝隙间的稳定穿越。此外，两个辅助预测模块——跨缝成功率分类器与可通行性预测器——进一步提升导航连续性与安全性。大量仿真与真实世界实验验证了该方法的有效性、泛化能力及实际鲁棒性。
原文摘要 · Abstract (English)
-Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches often assume regularly shaped gaps, leading to poor generalization and limited practicality. In this work, we present a fully vision-based, end-to-end framework that maps depth images directly to control commands, enabling drones to traverse complex gaps within unseen environments. Operating in the Special Euclidean group SE(3), where position and orientation are tightly coupled, the framework leverages differentiable simulation, a Stop-Gradient operator, and a Bimodal Initialization Distribution to achieve stable traversal through consecutive gaps. Two auxiliary prediction modules-a gap-crossing success classifier and a traversability predictor-further enhance continuous navigation and safety. Extensive simulation and real-world experiments demonstrate the approach's effectiveness, generalization capability, and practical robustness.
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