Xinze Niu

2 accepted papers

2026

Swooper: Learning High-Speed Aerial Grasping With a Simple Gripper

Ziken Huang, Xinze Niu, Bowen Chai, Renbiao Jin, Danping Zou

RA-L 2026

High-speed aerial grasping presents significant challenges due to the high demands on precise, responsive flight control and coordinated gripper manipulation. In this work, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Swooper</i>, a dee

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2026

Vector Field Augmented Differentiable Policy Learning for Vision-Based Drone Racing

Yang Su, Feng Yu, Yu Hu, Xinze Niu, Linzuo Zhang, Fangyu Sun +1

RA-L 2026

Autonomous drone racing in complex environments requires agile, high-speed flight while maintaining reliable obstacle avoidance. Differentiable-physics-based policy learning has recently demonstrated high sample efficiency and remarkable performance across various tasks, including agile drone flight

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