Distributed Model-Based Diffusion For Scalable Multi-Robot Trajectory Optimization
Distributed Model-Based Diffusion is proposed, a distributed server-robot framework that decomposes the reverse diffusion process into local conditional reverse diffusion processes that enables each robot to iteratively perform denoising independently within its own control subspace while conditioning on the current trajectory estimates of the other robots that are aggregated and broadcast by the server.