MA-SafeDiffuser Extends Diffusion Planning to Multi-Agent Safety
Summary:MA-SafeDiffuser extends SafeDiffuser’s control-barrier-function (CBF) constraints to diffusion-based trajectory planning for multiple agents. The authors formulate joint safety as an intersection of per-agent and pairwise barrier sets, then derive centralized and communication-aware constrained denoising procedures with finite-time invariance guarantees under mild assumptions. Time-varying specifications and liveness CBFs are introduced to mitigate local traps and deadlocks. Experiments on a mul
## Core Contribution: From Individual Safety to Joint Invariance MA-SafeDiffuser targets a central limitation of diffusion-based planners: the trajectory-generation process itself does not naturally preserve safety constraints. The method embeds control barrier functions (CBFs) directly into every reverse-diffusion denoising step, ensuring that generated trajectories satisfy both individual constraints and pairwise inter-agent constraints rather than relying on a collision check after planning. The joint safe set is formalized as: \[ \mathcal{S}_{joint}=\bigcap_{i=1}^{N}\mathcal{S}_{i}\cap\bigcap_{i **Source**: [ArXiv AI (cs.AI)](https://arxiv.org/abs/2611.05524) (2026-11-05) — END —
Tags: #Diffusion Models #Multi-Agent Planning #Control Barrier Functions #Safe Reinforcement Learning #Diffusion Planner
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