用扩散模型让机器人集群自主覆盖区域，效果优于现有方法。
Scalable Multi Agent Diffusion Policies for Coverage Control
- 以扩散模型生成高维动作分布，捕捉机器人间协作依赖。
- 在不同密度和环境下的覆盖率任务中，性能超越主流基线。
- 适合需要多智能体协同的现实场景，如巡检、搜索任务。
我们提出MADP，一种基于扩散模型的去中心化机器人集群协作新方法。MADP利用扩散模型生成复杂且高维的动作分布，以捕捉各机器人动作间的相互依赖关系。每个机器人基于自身观测与同伴传递的感知嵌入融合表示进行策略采样。为评估该方法，我们使用由一个全知专家通过模仿学习训练的MADP策略，让一群全向移动机器人完成覆盖率控制这一典型多智能体导航任务。扩散过程由空间变换器架构参数化，支持去中心化推理。我们在不同数量、位置及重要性密度函数方差条件下测试系统，模拟真实覆盖率任务中的鲁棒性需求。实验表明，该模型继承了扩散模型的优良特性，在不同机器人密度和环境间具有强泛化能力，持续优于当前最先进基线。
原文摘要 · Abstract (English)
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.
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