Accelerating Mixed Discrete-Continuous Motion Planning via Neural Graphs of Convex Sets

• URL: http://arxiv.org/abs/2608.15440v1
• Date: Sat, 15 Aug 2026 22:51:56 GMT
• Title: Accelerating Mixed Discrete-Continuous Motion Planning via Neural Graphs of Convex Sets
• Abstract summary: Motion planning problems such as collision-free navigation and contact-rich manipulation can be naturally formulated as optimization problems.<n>The Graphs of Convex Sets (GCS) framework offers a practical solution.<n>We replace the costly convex relaxation step required by nominal GCS with a single forward pass through a Graph Attention Network.<n>A ranking network then orders these candidates by their estimated cost.
• Score: 4.288763738857392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motion planning problems such as collision-free navigation and contact-rich manipulation can be naturally formulated as optimization problems that couple discrete decisions with continuous trajectories. The Graphs of Convex Sets (GCS) framework offers a practical solution to these problems. It represents discrete decisions as nodes of a graph and encodes continuous trajectories in the edges connecting them. However, the resulting optimization subproblems can become computationally prohibitive for online replanning. In this work, we propose a learning-based strategy to mitigate this limitation. Specifically, we replace 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. A lightweight ranking network then orders these candidates by their estimated trajectory cost. Evaluating them in this order, we terminate our search early while still recovering a near-optimal motion plan. We validate the resulting pipeline across diverse robotic tasks, including collision-free motion planning for a 3D quadrotor and a 7-DoF manipulator, and planning through contact for planar pushing. Across both convex and non-convex cost and constraint settings, our approach yields up to two orders of magnitude speedup over nominal GCS while maintaining a 100% success rate, at the cost of some suboptimality in the recovered solutions. Code implementations and video demonstrations can be found at https://neural-gcs.github.io/.

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