ICML 2025

Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Jinhao Liang 1 Jacob K. Christopher 1 Sven Koenig 2 Ferdinando Fioretto 1
Abstract
Recent advances in diffusion models hold signifi-
cant potential in robotics, enabling the generation
of diverse and smooth trajectories directly from
raw representations of the environment. Despite
this promise, applying diffusion models to mo-
tion planning remains challenging due to their
difficulty in enforcing critical constraints, such
as collision avoidance and kinematic feasibility.
These limitations become even more pronounced
in Multi-Robot Motion Planning (MRMP), where
multiple robots must coordinate in shared spaces.
To address these challenges, this work proposes
Simultaneous MRMP Diffusion (SMD), a novel
approach integrating constrained optimization
into the diffusion sampling process to produce
collision-free, kinematically feasible trajectories.
Additionally, the paper introduces a comprehen-
sive MRMP benchmark to evaluate trajectory
planning algorithms across scenarios with vary-
ing robot densities, obstacle complexities, and
motion constraints. Experimental results show
SMD consistently outperforms classical and other
learning-based motion planners, achieving higher
success rates and efficiency in complex multi-
robot environments. The code and implementa-
tion are available at https://github.com/
RAISELab-atUVA/Diffusion-MRMP.
1. Introduction
Multi-Robot Motion Planning (MRMP) is a fundamental
problem in robotics and autonomous systems, where the
goal is to compute collision-free paths for multiple robots
navigating shared environments (Luo et al., 2024; Shaoul
et al., 2025). MRMP has widespread applications, from
autonomous vehicles to warehouse logistics and search-and-
1Department of Computer Science, University of Virginia, Char-
lottesville, VA 22903, USA 2Department of Computer Science,
University of California, Irvine, CA 92697, USA. Correspondence
to: Ferdinando Fioretto <fioretto@vir...