# D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

arXiv: https://arxiv.org/abs/2503.12204 · HTML: https://mlsanity.com/paper/2503.12204/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## 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, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [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)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Multi-Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2410.03072/) (2024)
- [Model-Based Diffusion for Trajectory Optimization](https://mlsanity.com/paper/2407.01573/) (2024)
- [MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](https://mlsanity.com/paper/2010.11061/) (2020)
- [EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments](https://mlsanity.com/paper/2011.04183/) (2020)
- [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)
- [Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models](https://mlsanity.com/paper/2412.17993/) (2024)
- [Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers](https://mlsanity.com/paper/2401.09241/) (2024)
- [Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control](https://mlsanity.com/paper/1809.04230/) (2018)
- [Quick Multi-Robot Motion Planning by Combining Sampling and Search](https://mlsanity.com/paper/2203.00315/) (2022)
- [AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments](https://mlsanity.com/paper/2303.04856/) (2023)
- [Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals](https://mlsanity.com/paper/2302.06547/) (2023)
- [RLSS: Real-time, Decentralized, Cooperative, Networkless Multi-Robot Trajectory Planning using Linear Spatial Separations](https://mlsanity.com/paper/2302.12863/) (2023)

## Abstract (arXiv)

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. Code and video: https://github.com/proroklab/d4orm