DAP：面向多模态存储的基于扩散的可供性预测
DAP: Diffusion-based Affordance Prediction for Multi-modality Storage
AI总结：
本文提出基于扩散的可供性预测流程DAP，通过先识别可放置区域再计算相对位姿的两步方法解决多模态物体存储问题，在RPDiff基准上性能与训练效率均优于现有方法，并展示了现实应用中的数据效率。
摘要：
解决存储问题——即物体必须以精确的朝向和位置被准确放入容器中——提出了一个超越传统重排任务的独特挑战。这些挑战主要源于对细粒度6D操作的需求以及解空间固有的多模态性，即同一存储容器存在多个可行的目标配置。我们提出了一种新颖的基于扩散的可供性预测（DAP）流程，用于多模态物体存储问题。DAP采用两步方法，首先在容器上识别可放置区域，然后精确计算物体与该区域之间的相对位姿。现有方法要么难以应对多模态问题，要么训练计算密集。我们的实验表明，DAP在性能和训练效率上均优于当前最先进的RPDiff，并在RPDiff基准上取得了显著成果。此外，我们的实验展示了DAP在实际应用中的数据效率，这是对现有仿真驱动方法的进步。我们的贡献填补了机器人操作研究中的一个空白，提供了一种既计算高效又能处理现实世界变异性的解决方案。代码和补充材料可在以下网址找到：https://github.com/changhaonan/DPS.git。
英文摘要：
Solving storage problem: where objects must be accurately placed into containers with precise orientations and positions, presents a distinct challenge that extends beyond traditional rearrangement tasks. These challenges are primarily due to the need for fine-grained 6D manipulation and the inherent multi-modality of solution spaces, where multiple viable goal configurations exist for the same storage container. We present a novel Diffusion-based Affordance Prediction (DAP) pipeline for the multi-modal object storage problem. DAP leverages a two-step approach, initially identifying a placeable region on the container and then precisely computing the relative pose between the object and that region. Existing methods either struggle with multi-modality issues or computation-intensive training. Our experiments demonstrate DAP's superior performance and training efficiency over the current state-of-the-art RPDiff, achieving remarkable results on the RPDiff benchmark. Additionally, our experiments showcase DAP's data efficiency in real-world applications, an advancement over existing simulation-driven approaches. Our contribution fills a gap in robotic manipulation research by offering a solution that is both computationally efficient and capable of handling real-world variability. Code and supplementary material can be found at: https://github.com/changhaonan/DPS.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
