A Global-Local Graph Attention Network for Deformable Linear Objects Dynamic Interaction With Environment

Accurately modeling the interactions between deformable linear objects (DLOs) and their environments is crucial for active deformation control by robot manipulators. Graph Neural Networks (GNNs) have shown immense potential in the particle-based dynamics of DLOs. However, most existing studies propagate particle information in sequence, ignoring that particle motions, including the distal particle, correlate strongly with each other and the interaction state. In this paper, a global and local attention dynamic model named GladNet is designed based on GNNs and the attention mechanism to aggregate information among particles and focus on the interaction particles for DLO interaction with the environment. Specifically, a global virtual node is proposed to deliver particle information and shorten the propagation path for the first time, which connects all the particles and aggregates global information. When the DLOs and the obstacle boundary particles are close, an edge is established between them to capture the interaction state. Moreover, we group all the particles by $k$-hop neighbors and design a HopSA module that combines hop attention and self-attention to discover the correlates among adjacent particles. Experimental results on simulation and real-world data show that the proposed GladNet network's predictive accuracy outperforms baseline models, especially in long-term prediction.