6 papers
Drone Soccer: Learning to Manipulate with Multicopter Downwash
Neelay Joglekar, Bavin Saravanan, Yutong Wang +3
Although multicopter drones are traditionally designed for "perception-only" tasks, like mapping and exploration, recent work has sought to develop Unmanned Aerial Manipulators (UA…
Aerial Manipulation in the Wild with Onboard Perception, Policy Learning, and Whole-Body Control
Yuanzhu Zhan, Yufei Jiang, Zemu Zhang +1
Aerial manipulation in outdoor environments remains challenging due to the simultaneous requirements of reliable state estimation, stable aerial motion, and precise manipulation un…
AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning
Yutong Wang, Dongjae Lee, Xiaofeng Guo +9
Standardized benchmarks have played a central role in advancing robot manipulation learning, yet most focus on ground-supported manipulation systems, which limits their applicabili…
Aerial Manipulation with Contact-Aware Onboard Perception and Hybrid Control
Yuanzhu Zhan, Yufei Jiang, Muqing Cao +1
Aerial manipulation (AM) promises to move Unmanned Aerial Vehicles (UAVs) beyond passive inspection to contact-rich tasks such as grasping, assembly, and in-situ maintenance. Most…
Imperative MPC: An End-to-End Self-Supervised Learning with Differentiable MPC for UAV Attitude Control
Haonan He, Yuheng Qiu, Junyi Geng
Modeling and control of nonlinear dynamics are critical in robotics, especially in scenarios with unpredictable external influences and complex dynamics. Traditional cascaded modul…
A Self-Supervised Learning Approach with Differentiable Optimization for UAV Trajectory Planning
Yufei Jiang, Yuanzhu Zhan, Harsh Vardhan Gupta +2
While Unmanned Aerial Vehicles (UAVs) have gained significant traction across various fields, path planning in 3D environments remains a critical challenge, particularly under size…