提出统一框架，让机器人高效展开并标准化衣物，提升后续折叠效率。
Learning Efficient Robotic Garment Manipulation with Standardization
- 双臂多姿态策略结合动态甩展与精准抓放，实现快速展开。
- 模拟中覆盖度提升3.9%，交并比提高5.2%，关键点距离降低7.09%。
- 适合需自动化衣物处理的工业场景，如仓储、智能衣柜。
衣物操作对机器人而言极具挑战，因其复杂的动力学特性与易自遮挡问题。现有高效展开方法常忽视展开过程中衣物标准化的重要性，而标准化可显著简化后续折叠、熨烫与包装任务。本文提出APS-Net，一种融合展开与标准化的统一框架。该方法采用双臂多姿态策略，结合动态甩展快速展开皱褶衣物，并通过抓取-放置（p and p）实现精确对齐。标准化目标不仅最大化表面覆盖率，还确保衣物形状与朝向符合预设规范。为支持有效学习，设计了一种分解式奖励函数，包含覆盖率（Cov）、关键点距离（KD）与交并比（IoU）指标。同时引入空间动作掩码与动作优化模块，提升操作点与动作选择效率。仿真结果表明，相较于最先进方法，长袖衣物展开在覆盖率上提升3.9%，交并比提高5.2%，关键点距离下降0.14（相对减少7.09%）。真实世界折叠实验进一步验证了标准化能显著简化折叠流程。
原文摘要 · Abstract (English)
Garment manipulation is a significant challenge for robots due to the complex dynamics and potential self-occlusion of garments. Most existing methods of efficient garment unfolding overlook the crucial role of standardization of flattened garments, which could significantly simplify downstream tasks like folding, ironing, and packing. This paper presents APS-Net, a novel approach to garment manipulation that combines unfolding and standardization in a unified framework. APS-Net employs a dual-arm, multi-primitive policy with dynamic fling to quickly unfold crumpled garments and pick-and-place (p and p) for precise alignment. The purpose of garment standardization during unfolding involves not only maximizing surface coverage but also aligning the garment's shape and orientation to predefined requirements. To guide effective robot learning, we introduce a novel factorized reward function for standardization, which incorporates garment coverage (Cov), keypoint distance (KD), and intersection-over-union (IoU) metrics. Additionally, we introduce a spatial action mask and an Action Optimized Module to improve unfolding efficiency by selecting actions and operation points effectively. In simulation, APS-Net outperforms state-of-the-art methods for long sleeves, achieving 3.9 percent better coverage, 5.2 percent higher IoU, and a 0.14 decrease in KD (7.09 percent relative reduction). Real-world folding tasks further demonstrate that standardization simplifies the folding process. Project page: see https://hellohaia.github.io/APS/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。