43 citations · 43 across the 13 of their papers we have counts for
13 papers
FlowPilot: Real-Time World-Action Modeling for Agile UAV Navigation
Runqing Wang, Ding Yu, Pengyuan Min +6
We present FlowPilot, a compact world-action model for real-time onboard UAV navigation from depth. Unlike map-then-optimize pipelines that require local reconstruction or end-to-e…
AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight
Xinhong Zhang, Qiyuan Zhu, Yubo Huang +8
Language-conditioned quadrotor flight requires a policy to ground semantic goals, anticipate the visual consequences of ego-motion, and output control references that remain smooth…
Asymmetric physics enables efficient learning in quadrupedal robot swarms
Yuang Zhang, Yunlong Song, Zhihao He +7
Animal collectives navigate cluttered environments through local coordination, yet robot swarms still struggle to reproduce this capability in the physical world. End-to-end learni…
Autonomous FPV Flight with Translational Optical Flow and Uncertainty Mask
Yang Deng, Yu Hu, Feng Yu +2
Autonomous FPV quadrotor flight in complex environments using a monocular RGB camera as the sole exteroceptive sensor remains a fundamental challenge. Recent research has shown tha…
PrecisionCUA: Iterative Visual Refinement for Pixel-Precise Cursor Grounding in Code Editors
Himangi Mittal, Gaurav Mittal, Nelson Daniel Troncoso +1
Computer Use Agents (CUAs) fundamentally rely on graphical user interface (GUI) grounding to translate language instructions into executable screen actions, but editing-level groun…
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,…
Vector Field Augmented Differentiable Policy Learning for Vision-Based Drone Racing
Yang Su, Feng Yu, Yu Hu +4
Autonomous drone racing in complex environments requires agile, high-speed flight while maintaining reliable obstacle avoidance. Differentiable-physics-based policy learning has re…
AI-IO: An Aerodynamics-Inspired Real-Time Inertial Odometry for Quadrotors
Jiahao Cui, Feng Yu, Linzuo Zhang +2
Inertial Odometry (IO) has gained attention in quadrotor applications due to its sole reliance on inertial measurement units (IMUs), attributed to its lightweight design, low cost,…
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…
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…