6 papers
Flying in Clutter on Monocular RGB by Learning in 3D Radiance Fields with Domain Adaptation
Xijie Huang, Jinhan Li, Tianyue Wu +3
Modern autonomous navigation systems predominantly rely on lidar and depth cameras. However, a fundamental question remains: Can flying robots navigate in clutter using solely mono…
Reactive Aerobatic Flight via Reinforcement Learning
Zhichao Han, Xijie Huang, Zhuxiu Xu +5
Quadrotors have demonstrated remarkable versatility, yet their full aerobatic potential remains largely untapped due to inherent underactuation and the complexity of aggressive man…
Automatic Generation of Aerobatic Flight in Complex Environments via Diffusion Models
Yuhang Zhong, Anke Zhao, Tianyue Wu +2
Performing striking aerobatic flight in complex environments demands manual designs of key maneuvers in advance, which is intricate and time-consuming as the horizon of the traject…
FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations
Junxiao Lin, Shuhang Ji, Yuze Wu +3
How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system's dual-task requirement: generat…
Flying on Point Clouds with Reinforcement Learning
Guangtong Xu, Tianyue Wu, Zihan Wang +2
A long-cherished vision of drones is to autonomously traverse through clutter to reach every corner of the world using onboard sensing and computation. In this paper, we combine on…
Whole-Body Control Through Narrow Gaps From Pixels To Action
Tianyue Wu, Yeke Chen, Tianyang Chen +2
Flying through body-size narrow gaps in the environment is one of the most challenging moments for an underactuated multirotor. We explore a purely data-driven method to master thi…