  Sampling-based motion planning is a popular approach in robotics for finding
paths in continuous configuration spaces. Checking collision with obstacles is
the major computational bottleneck in this process. We propose new
learning-based methods for reducing collision checking to accelerate motion
planning by training graph neural networks (GNNs) that perform path exploration
and path smoothing. Given random geometric graphs (RGGs) generated from batch
sampling, the path exploration component iteratively predicts collision-free
edges to prioritize their exploration. The path smoothing component then
optimizes paths obtained from the exploration stage. The methods benefit from
the ability of GNNs of capturing geometric patterns from RGGs through batch
sampling and generalize better to unseen environments. Experimental results
show that the learned components can significantly reduce collision checking
and improve overall planning efficiency in challenging high-dimensional motion
planning tasks.
