4 papers
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…
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…