Yufei Yan

Fanxing Li, Shengyang Wang, Yuxiang Huang et al.

Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications. When planning the trajectory, existing approaches both on optimization and learning typically regard quadrotor as a point-mass model, giving path or velocity commands then tracking the commands by outer-loop controller. However, at high speeds, planned trajectories sometimes become dynamically infeasible in actual flight, which beyond the capacity of controller. In this paper, we propose a novel end-to-end policy that directly maps depth images to low-level bodyrate commands by reinforcement learning via differentiable simulation. The high-fidelity simulation in training after parameter identification significantly reduces all the gaps between training, simulation and real world. Analytical process by differentiable simulation provides accurate gradient to ensure efficiently training the low-level policy without expert guidance. The policy employs a lightweight and the most simple inference pipeline that runs without explicit mapping, backbone networks, primitives, recurrent structures, or backend controllers, nor curriculum or privileged guidance. By inferring low-level command directly to the hardware controller, the method enables full flight envelope control and avoids the dynamic-infeasible issue.Experimental results demonstrate that the proposed approach achieves the highest success rate and the lowest jerk among state-of-the-art baselines across multiple benchmarks. The policy also exhibits strong generalization, successfully deploying zero-shot in unseen, outdoor environments while reaching speeds of up to 7.5m/s as well as stably flying in the super-dense forest. This work is released at https://github.com/Fanxing-LI/avoidance.

Fanxing Li, Shengyang Wang, Fangyu Sun et al.

Existing FPV object tracking methods heavily rely on handcrafted modular pipelines, which incur high onboard computation and cumulative errors. While learning-based approaches have mitigated computational delays, most still generate only high-level trajectories (position and yaw). This loose coupling with a separate controller sacrifices precise attitude control; consequently, even if target is localized precisely, accurate target estimation does not ensure that the body-fixed camera is consistently oriented toward the target, it still probably degrades and loses target when tracking high-maneuvering target. To address these challenges, we present StableTracker, a learning-based control policy that enables quadrotors to robustly follow a moving target from arbitrary viewpoints. The policy is trained using backpropagation-through-time via differentiable simulation, allowing the quadrotor to keep a fixed relative distance while maintaining the target at the center of the visual field in both horizontal and vertical directions, thereby functioning as an autonomous aerial camera. We compare StableTracker against state-of-the-art traditional algorithms and learning baselines. Simulation results demonstrate superior accuracy, stability, and generalization across varying safe distances, trajectories, and target velocities. Furthermore, real-world experiments on a quadrotor with an onboard computer validate the practicality of the proposed approach.

Fanxing Li, Fangyu Sun, Tianbao Zhang et al.

First-order reinforcement learning with differentiable simulation is promising for quadrotor control, but practical progress remains fragmented across task-specific settings. To support more systematic development and evaluation, we present a unified differentiable framework for multi-task quadrotor control. The framework is wrapped, extensible, and equipped with deployment-oriented dynamics, providing a common interface across four representative tasks: hovering, tracking, landing, and racing. We also present the suite of first-order learning algorithms, where we identify two practical bottlenecks of standard first-order training: limited state coverage caused by horizon initialization and gradient bias caused by partially non-differentiable rewards. To address these issues, we propose Amended Backpropagation Through Time (ABPT), which combines differentiable rollout optimization, a value-based auxiliary objective, and visited-state initialization to improve training robustness. Experimental results show that ABPT yields the clearest gains in tasks with partially non-differentiable rewards, while remaining competitive in fully differentiable settings. We further provide proof-of-concept real-world deployments showing initial transferability of policies learned in the proposed framework beyond simulation.