用扩散模型解决多机械臂协同运动规划，提升效率与可扩展性
Diffusion-Guided Multi-Arm Motion Planning
- 基于多智能体路径寻找思想，将多臂规划分解为单臂生成与成对碰撞处理
- 通过两个条件扩散模型分别生成可行轨迹与双臂动态，实现高效碰撞规避
- 无需海量多臂数据，可扩展至更多机械臂，适合实际工业协作场景
多机械臂运动规划是实现共享空间中复杂长时任务的关键，但现有方法因状态空间指数级增长及对大规模训练数据的依赖而难以扩展。受多智能体路径寻找（MAPF）启发，我们将规划问题分解为单臂问题与碰撞解决，提出一种扩散引导的多臂规划器（DG-MAP）。鉴于碰撞主要为成对发生，我们训练了两个条件扩散模型：一个用于生成可行的单臂轨迹，另一个用于建模双臂动态以实现有效的成对碰撞规避。通过在MAPF-inspired结构中集成这两个生成模型，我们的规划器能高效扩展至更多机械臂。在不同团队规模下对比多种学习方法的评估表明，该方法具有有效性与实用性。项目网站见 https://diff-mapf-mers.csail.mit.edu
原文摘要 · Abstract (English)
Multi-arm motion planning is fundamental for enabling arms to complete complex long-horizon tasks in shared spaces efficiently but current methods struggle with scalability due to exponential state-space growth and reliance on large training datasets for learned models. Inspired by Multi-Agent Path Finding (MAPF), which decomposes planning into single-agent problems coupled with collision resolution, we propose a novel diffusion-guided multi-arm planner (DG-MAP) that enhances scalability of learning-based models while reducing their reliance on massive multi-arm datasets. Recognizing that collisions are primarily pairwise, we train two conditional diffusion models, one to generate feasible single-arm trajectories, and a second, to model the dual-arm dynamics required for effective pairwise collision resolution. By integrating these specialized generative models within a MAPF-inspired structured decomposition, our planner efficiently scales to larger number of arms. Evaluations against alternative learning-based methods across various team sizes demonstrate our method's effectiveness and practical applicability. Project website can be found at https://diff-mapf-mers.csail.mit.edu
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