9 papers
Simple but Stable, Fast and Safe: Achieve End-to-end Control by High-Fidelity Differentiable Simulation
Fanxing Li, Shengyang Wang, Yuxiang Huang +5
Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications. When planning the trajectory, existing approaches both on…
E2E-Fly: An Integrated Training-to-Deployment System for End-to-End Quadrotor Autonomy
Fangyu Sun, Fanxing Li, Linzuo Zhang +5
Training and transferring learning-based policies for quadrotors from simulation to reality remains challenging due to inefficient visual rendering, physical modeling inaccuracies,…
Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
Linzuo Zhang, Yu Hu, Feng Yu +3
-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, tra…
VisFly-Lab: Unified Differentiable Framework for First-Order Reinforcement Learning of Quadrotor Control
Fanxing Li, Fangyu Sun, Tianbao Zhang +5
First-order reinforcement learning with differentiable simulation is promising for quadrotor control, but practical progress remains fragmented across task-specific settings. To su…
Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
Fangyu Sun, Fanxing Li, Yu Hu +4
Autonomous drone racing has attracted increasing interest as a research topic for exploring the limits of agile flight. However, existing studies primarily focus on obstacle-free r…
CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation
Dexin Zuo, Ang Li, Wei Wang +2
Object 6D pose estimation, a crucial task for robotics and augmented reality applications, becomes particularly challenging when dealing with novel objects whose 3D models are not…