We present Distributed Model-Based Diffusion (DMBD), a scalable server-robot framework for multi-robot trajectory optimization. By distributing the reverse diffusion process, DMBD enables each robot to perform local conditional denoising using only local information while conditioning on the trajectory estimates from other robots. We characterize the relationship between the global and local conditional denoising updates by deriving theoretical error bounds. Extensive simulations across diverse scenarios demonstrate that DMBD outperforms existing baselines.
Introduction
Multi-robot trajectory optimization is critical for applications such as warehouse automation, search and rescue, and autonomous driving. Traditional centralized approaches suffer from high computational complexity and communication bottlenecks, while decentralized methods often sacrifice solution quality. Diffusion models have recently emerged as powerful tools for trajectory generation, but their application to multi-robot systems remains underexplored.
Related Work
Sampling-based trajectory optimization methods have been widely used in robotics, with approaches ranging from gradient-based optimization to probabilistic inference. Diffusion models offer a new paradigm by learning to reverse a noising process to generate high-quality trajectories. However, existing diffusion-based methods typically assume a centralized setting and do not scale well to multi-robot scenarios.
Methodology
Sampling-Based Trajectory Optimization
We define a target distribution over the space of trajectories with temperature β, where we define a global cost function that captures both individual robot objectives and inter-robot collision avoidance constraints.
Distributed Conditional Denoising
The key innovation of DMBD is distributing the reverse diffusion process across robots. Each robot maintains its own local trajectory estimate and performs conditional denoising using only locally available information, while a central server aggregates and broadcasts trajectory estimates from all robots.
Theoretical Error Bounds
We derive theoretical bounds on the discrepancy between global and local conditional denoising updates. These bounds characterize how local approximations affect overall solution quality and provide guarantees on convergence.
Experimental Results
We evaluate DMBD across diverse multi-robot scenarios including formation control, collision avoidance, and target tracking. The proposed method consistently outperforms baselines in terms of trajectory quality, computational efficiency, and scalability. DMBD achieves lower collision rates and smoother trajectories while requiring significantly less communication bandwidth than centralized approaches.
Conclusions
We present Distributed Model-Based Diffusion (DMBD), a scalable server-robot framework for multi-robot trajectory optimization. By distributing the reverse diffusion process, DMBD enables each robot to perform local conditional denoising using only local information while conditioning on the trajectory estimates from other robots. We characterize the relationship between the global and local conditional denoising updates by deriving theoretical error bounds. Extensive simulations across diverse scenarios demonstrate that DMBD outperforms existing baselines.
Frequently Asked Questions
How does DMBD differ from centralized diffusion-based trajectory optimization? DMBD distributes the reverse diffusion process across robots, allowing each robot to perform local denoising using only local information while still conditioning on other robots' estimates.
What theoretical guarantees does DMBD provide? We derive error bounds that characterize the discrepancy between global and local conditional denoising updates, providing convergence guarantees for the distributed approach.
What are the key benefits of the server-robot architecture? The architecture reduces communication bandwidth requirements and computational load on individual robots while maintaining solution quality through global conditioning.
In which scenarios does DMBD perform best? DMBD shows strongest performance in scenarios with many robots requiring coordination, such as formation control and collision avoidance, where scalability is critical.