Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion
Manav Vora, Gokul Puthumanaillam, Aditya Penumarti, Jane Shin, Melkior Ornik · 2026 arXiv · PDF
Abstract
Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating individual motions without expensive joint computation. We introduce Akule, a sparse interaction diffusion framework that augments a frozen single-robot prior with three learned modules: a reusable pairwise correction model \(R_{\phi}\), a contextual selector \(U_{\psi}\) that determines which corrections to evaluate, and a composer \(G_{\omega}\) that combines their effects through context-dependent signed weights. This decomposition separates which interactions merit computation from how their effects should be combined into coordinated denoising updates. We train \(R_{\phi}\) using matched pairwise trajectory optimizations, then learn \(G_{\omega}\) and \(U_{\psi}\) from physically optimized joint trajectories without requiring a prescribed ground-truth interaction graph. The resulting joint proposals are passed to a downstream search-based planner for validation and repair. Across diverse MRMP benchmarks, Akule achieves competitive planning success, complete-planner speedups of up to \(25\times\), zero-shot population transfer, and successful planning with up to \(256\) robots. Ablations further demonstrate that learned selection and composition reduce pre-repair conflicts and downstream repair effort relative to heuristic selection and uniform summation.
TL;DR
Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating individual motions without expensive joint computation. We introduce Akule, a sparse interaction diffusion framework that augments a frozen single-robot prior with three learned modules: a reusable pairwise correction model \(R_{\phi}\), a contextual selector \(U_{\psi}\) that determines which corrections to evaluate, and a composer \(G_{\omega}\) that combines their effects through context-dependent signed weights.