6 accepted papers
Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2-node and 3-node interactions, but incur cubic computational cost. Existing efficiency methods typically reduce this burden at the expense of expressivity. We propose Co-Sparsify, a connect
Continuous blood pressure (BP) monitoring is essential for timely diagnosis and intervention in critical care settings. However, BP varies significantly across individuals, this inter-patient variability motivates the development of personalized models tailored to each patient’s physiology. In this…
Deformable linear objects (DLOs), such as ropes, cables, and rods, are common in various scenarios, and accurate occlusion reconstruction of them is crucial for effective robotic manipulation. Previous studies for DLO reconstruction either rely on supervised learning, which is limited by the availab
Automatic assembly of board-to-board (BTB) connectors remains a significant challenge in smartphone manufacturing due to severe visual occlusion, tight assembly tolerances, and process constraints that prohibit separate visual adjustment stations. This letter proposes Visual-Privileged Co-Learning (
Previous work has shown that the set of equilibrium shapes of a rod is a smooth 6-dimensional manifold. We develop the Kirchhoff rod model in a differential equation form using Darboux vector that easily adds distributed force and intrinsic curvature. It can be seen that in this letter, the manifold
Complex assembly tasks remain huge challenge for robots because the traditional control methods rely on complicated contact state analysis. Reinforcement learning (RL) becomes one of the preferred embodiments to construct the control strategy of complex tasks. In this paper, the method of model-driv…