# Multi-Robot Motion Planning with Diffusion Models

arXiv: https://arxiv.org/abs/2410.03072 · HTML: https://mlsanity.com/paper/2410.03072/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://mlsanity.com/paper/2308.01557/) (2023)
- [Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models](https://mlsanity.com/paper/2401.03360/) (2023)
- [EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding](https://mlsanity.com/paper/2010.01367/) (2020)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019)
- [Data-driven Planning via Imitation Learning](https://mlsanity.com/paper/1711.06391/) (2017)
- [Adaptive Online Replanning with Diffusion Models](https://mlsanity.com/paper/2310.09629/) (2023)
- [Diffusion Model for Planning: A Systematic Literature Review](https://mlsanity.com/paper/2408.10266/) (2024)
- [Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search](https://mlsanity.com/paper/1909.13352/) (2019)
- [Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph](https://mlsanity.com/paper/2409.16275/) (2024)
- [Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning](https://mlsanity.com/paper/1305.2889/) (2013)
- [Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances](https://mlsanity.com/paper/2012.09052/) (2020)
- [Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation](https://mlsanity.com/paper/2203.11618/) (2022)
- [Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning](https://mlsanity.com/paper/2303.01619/) (2023)
- [Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences](https://mlsanity.com/paper/2404.00143/) (2024)
- [CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models](https://mlsanity.com/paper/2009.09942/) (2020)
- [Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality](https://mlsanity.com/paper/2405.01772/) (2024)

## Built on by (most cited first, 30 of 38)

- [Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models](https://mlsanity.com/paper/2502.03607/) (2025)
- [Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation](https://mlsanity.com/paper/2506.01121/) (2025)
- [Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning](https://mlsanity.com/paper/2508.20095/) (2025)
- [Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternatives](https://mlsanity.com/paper/2505.05787/) (2025)
- [Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning](https://mlsanity.com/paper/2506.00867/) (2025)
- [Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models](https://mlsanity.com/paper/2412.17993/) (2024)
- [Projected Coupled Diffusion for Test-Time Constrained Joint Generation](https://mlsanity.com/paper/2508.10531/) (2025)
- [Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion](https://mlsanity.com/paper/2602.02722/) (2026)
- [Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling](https://mlsanity.com/paper/2505.18017/) (2025)
- [SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer](https://mlsanity.com/paper/2505.15679/) (2025)
- [High-Performance Self-Supervised Learning by Joint Training of Flow Matching](https://mlsanity.com/paper/2512.19729/) (2025)
- [Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning](https://mlsanity.com/paper/2605.27697/) (2026)
- [Navigating Motion Agents in Dynamic and Cluttered Environments through LLM Reasoning](https://mlsanity.com/paper/2503.07323/) (2025)
- [Diffusion-Guided Multi-Arm Motion Planning](https://mlsanity.com/paper/2509.08160/) (2025)
- [U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation](https://mlsanity.com/paper/2602.23739/) (2026)
- [Compositional Visual Planning via Inference-Time Diffusion Scaling](https://mlsanity.com/paper/2603.02646/) (2026)
- [Refining Compositional Diffusion for Reliable Long-Horizon Planning](https://mlsanity.com/paper/2605.03075/) (2026)
- [DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising](https://mlsanity.com/paper/2606.15359/) (2026)
- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)
- [Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation](https://mlsanity.com/paper/2509.08157/) (2025)
- [Scalable Multi Agent Diffusion Policies for Coverage Control](https://mlsanity.com/paper/2509.17244/) (2025)
- [Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention](https://mlsanity.com/paper/2605.13296/) (2026)
- [D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations](https://mlsanity.com/paper/2503.12204/) (2025)
- [Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization](https://mlsanity.com/paper/2510.09204/) (2025)
- [Multi-Robot Motion Planning from Vision and Language using Heat-Inspired Diffusion](https://mlsanity.com/paper/2512.13090/) (2025)
- [Cost-Aware Diffusion Active Search](https://mlsanity.com/paper/2602.19538/) (2026)
- [Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning](https://mlsanity.com/paper/2604.06598/) (2026)
- [Muninn: Your Trajectory Diffusion Model But Faster](https://mlsanity.com/paper/2605.09999/) (2026)
- [Plan First, Diffuse Later: Extrinsic Graph Guidance for Long-Horizon Diffusion Planning](https://mlsanity.com/paper/2605.16863/) (2026)
- [Robots as Tokens: Unified Diffusion Transformer for Coordinated Multi-Robot Trajectory Generation](https://mlsanity.com/paper/2606.15550/) (2026)

## Abstract (arXiv)

Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robot diffusion models. In this paper, we propose a method for generating collision-free multi-robot trajectories that conform to underlying data distributions while using only single-robot data. Our algorithm, Multi-robot Multi-model planning Diffusion (MMD), does so by combining learned diffusion models with classical search-based techniques -- generating data-driven motions under collision constraints. Scaling further, we show how to compose multiple diffusion models to plan in large environments where a single diffusion model fails to generalize well. We demonstrate the effectiveness of our approach in planning for dozens of robots in a variety of simulated scenarios motivated by logistics environments. View video demonstrations and code at: https://multi-robot-diffusion.github.io/.