From the 1 of 13 linked papers with an AI index.
2 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations
Ziyang Zhang, Boyang Zhou, Zesong Yang +8
The paper proposes a goal‑conditioned stochastic path prior that learns a transferable distribution over local navigation paths from limited observations, enabling safe, multimodal…
Swooper: Learning High-Speed Aerial Grasping With a Simple Gripper
Ziken Huang, Xinze Niu, Bowen Chai +2
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 prop…
Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing
Feng Yu, Yu Hu, Yang Su +3
Most reinforcement learning(RL)-based methods for drone racing target fixed, obstacle-free tracks, leaving the generalization to unknown, cluttered environments largely unaddressed…
Mapless Collision-Free Flight via MPC using Dual KD-Trees in Cluttered Environments
Linzuo Zhang, Yu Hu, Yang Deng +2
Collision-free flight in cluttered environments is a critical capability for autonomous quadrotors. Traditional methods often rely on detailed 3D map construction, trajectory gener…
Seeing Through Pixel Motion: Learning Obstacle Avoidance from Optical Flow with One Camera
Yu Hu, Yuang Zhang, Yunlong Song +6
Optical flow captures the motion of pixels in an image sequence over time, providing information about movement, depth, and environmental structure. Flying insects utilize this inf…
Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics
Yuang Zhang, Yu Hu, Yunlong Song +2
Swarm navigation in cluttered environments is a grand challenge in robotics. This work combines deep learning with first-principle physics through differentiable simulation to enab…