Accelerating Mixed Discrete-Continuous Motion Planning via Neural Graphs of Convex Sets
This work replaces the costly convex relaxation step required by nominal GCS with a single forward pass through a Graph Attention Network that predicts a set of highly probable candidate paths through the graph, and generates a lightweight ranking network that orders these candidates by their estimated trajectory cost.