Learning-based approaches have shown promising performance for improving the efficiency of motion planning in robot manipulation problems, but mostly in the setting of static environments. For the more challenging problem of motion planning in dynamic environments, such as for multi-arm assembly tasks or human-robot interaction, motion planners need to consider the trajectories of the dynamic obstacles, and reason about the temporal-spatial interactions between the ego-arm and the other objects.…