3 accepted papers
Autonomous aerial systems are increasingly essential for transportation and delivery tasks in complex environments where ground access is limited or unsafe for direct placement. In airdrop missions, these platforms face the dual challenges of abrupt control mode switching and inherent system delays
Expressive motion planning for Aerial Manipulators (AMs) is essential for tackling complex manipulation tasks, yet achieving coupled trajectory planning adaptive to various tasks remains challenging, especially for those requiring aggressive maneuvers. In this work, we propose a novel whole-body int…
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and ef…