  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.
