Learning-based methods have shown promising performance for accelerating
motion planning, but mostly in the setting of static environments. For the more
challenging problem of planning in dynamic environments, such as multi-arm
assembly tasks and human-robot interaction, motion planners need to consider
the trajectories of the dynamic obstacles and reason about temporal-spatial
interactions in very large state spaces. We propose a GNN-based approach that
uses temporal encoding and imitation learning with data aggregation for
learning both the embeddings and the edge prioritization policies. Experiments
show that the proposed methods can significantly accelerate online planning
over state-of-the-art complete dynamic planning algorithms. The learned models
can often reduce costly collision checking operations by more than 1000x, and
thus accelerating planning by up to 95%, while achieving high success rates on
hard instances as well.

本文介绍了一种基于 GNN 的方法，利用时间编码和模仿学习与数据聚合来学习嵌入和边缘优先策略，以显着加速在线规划。

使用 GNN 和时间编码进行基于学习的动态环境运动规划

Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

Entity alignment is a crucial task in knowledge graph fusion. However, most
entity alignment approaches have the scalability problem. Recent methods
address this issue by dividing large KGs into small blocks for embedding and
alignment learning in each. However, such a partitioning and learning process
results in an excessive loss of structure and alignment. Therefore, in this
work, we propose a scalable GNN-based entity alignment approach to reduce the
structure and alignment loss from three perspectives. First, we propose a
centrality-based subgraph generation algorithm to recall some landmark entities
serving as the bridges between different subgraphs. Second, we introduce
self-supervised entity reconstruction to recover entity representations from
incomplete neighborhood subgraphs, and design cross-subgraph negative sampling
to incorporate entities from other subgraphs in alignment learning. Third,
during the inference process, we merge the embeddings of subgraphs to make a
single space for alignment search. Experimental results on the benchmark OpenEA
dataset and the proposed large DBpedia1M dataset verify the effectiveness of
our approach.

本文提出了一种可扩展的基于 GNN 的实体对齐方法，通过三个视角减少子图嵌入中的结构和对齐损失，并在实验中证实了该方法的有效性。