2025/09/21 by Frederic Vatnsdal, Romina García Camargo, Vatnsdal, Frederic +5
Economics, Econometrics and Finance · Engineering · Social Sciences · #Advanced Numerical Methods in Computational Mathematics #Credit Risk and Financial Regulations #FOS: Computer and information sciences #Labor Movements and Unions #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2509.17244
openalex publication_date 2025/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose MADP, a novel diffusion-model-based approach for collaboration in decentralized robot swarms. MADP leverages diffusion models to generate samples from complex and high-dimensional action distributions that capture the interdependencies between agents' actions. Each robot conditions policy sampling on a fused representation of its own observations and perceptual embeddings received from peers. To evaluate this approach, we task a team of holonomic robots piloted by MADP to address coverage control-a canonical multi agent navigation problem. The policy is trained via imitation learning from a clairvoyant expert on the coverage control problem, with the diffusion process parameterized by a spatial transformer architecture to enable decentralized inference. We evaluate the system under varying numbers, locations, and variances of importance density functions, capturing the robustness demands of real-world coverage tasks. Experiments demonstrate that our model inherits valuable properties from diffusion models, generalizing across agent densities and environments, and consistently outperforming state-of-the-art baselines.