Diffusion-Guided Multi-Arm Motion Planning
This work addresses the problem of enabling multiple robotic arms to perform complex tasks efficiently in shared spaces, representing an incremental improvement over existing methods by reducing reliance on large datasets.
The paper tackles the scalability challenge in multi-arm motion planning by proposing a diffusion-guided planner that decomposes the problem into single-arm trajectory generation and pairwise collision resolution, achieving efficient scaling to larger numbers of arms compared to alternative learning-based methods.
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