发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多智能体STL规划的通用能力与可扩展性的权衡问题，提出STL引导的扩散方法，实现了可泛化、可扩展且规划多样的多智能体规划，减少了安全违规。
AI 中文摘要
现实世界中的多智能体系统（如无人机集群、自动驾驶汽车、仓库机器人）必须在避免碰撞的同时满足丰富的时序任务，信号时序逻辑（Signal Temporal Logic，STL）可优雅地编码此类目标，但现有STL规划方法存在关键局限：最先进的基于优化的方法能处理任意STL规范，却面临可扩展性问题，随着智能体数量增加，计算上变得不切实际；基于学习的方法能高效处理大量智能体且规划速度快，但当部署时的目标与训练时使用的目标不同时表现不佳，且不支持为不同智能体分配不同规范（即异构性）或要求多个智能体协调的团队级规范，这种通用能力与可扩展性之间的根本权衡，是多智能体STL规划算法实际应用的挑战。为克服该挑战，我们提出一种新的、用于处理STL规范的多智能体规划扩散方法，利用STL的可微近似，将STL梯度集成到去噪过程中，使我们的方法可泛化到谓词位于训练覆盖的目标区域任意位置的新公式，同时达到与现有基于学习的方法相同的可扩展性；我们的方法支持异构规范，且通过使用扩散模型自然提升规划多样性，从而显著减少智能体间与安全相关的违规（如碰撞）；详细评估研究验证了STL引导的基于扩散的多智能体规划器在构建通用、可扩展且多样的规划方面的实用性，视频和代码可在指定URL获取。
英文摘要
Multi-agent systems in the real-world (e.g., drone swarms, autonomous cars, warehouse robots) must satisfy rich, temporal tasks while avoiding collisions. Signal Temporal Logic (STL) elegantly encodes such objectives, but current STL planning methods face critical limitations. State-of-the-art optimization-based approaches can handle arbitrary STL specifications but struggle with scalability, becoming computationally impractical as the number of agents grows. Learning-based methods efficiently handle a large number of agents with rapid planning times but fare poorly when deployment-time objectives differ from those used during training, and do not support planning tasks that require different specifications to be ascribed to different agents (i.e., heterogeneity) or team-level specifications requiring coordination of multiple agents. This fundamental trade-off between generalizability and scalability presents a challenge for realizing multi-agent STL planning algorithms in practice. To overcome this challenge, we introduce a new diffusion method for multi-agent planning with STL specifications. Using a differentiable approximation of STL, we integrate the STL gradient in the denoising process, making our approach generalizable to novel formulas whose predicates are placed anywhere within the goal region covered during training, while achieving the same scalability as existing learning-based methods. Our method supports heterogeneous specifications, and by using diffusion models, naturally enhances plan diversity, thereby significantly reducing safety-related violations (e.g., collisions) among agents. A detailed evaluation study justifies the utility of STL-guided diffusion-based multi-agent planners for constructing generalizable, scalable, and diverse plans. Videos and code are available at https://www.jeappen.com/diff-ma-stl/ and https://github.com/jeappen/diff-ma-stl .
CommentsAccepted for publication in IEEE Robotics and Automation Letters (RA-L), 2026
