让机器人学会先展开医疗服再帮忙穿，提升护理效率。
Evaluating the Pre-Dressing Step: Unfolding Medical Garments Via Imitation Learning
- 用模仿学习训练三种抓取动作，覆盖快慢速运动。
- 组合动作能有效提升折叠服装的展开程度。
- 适合需要自动化穿脱设备的医院或康复场景。
机器人辅助穿衣有望显著减轻患者与医护人员负担，提升临床效率。尽管已有研究进展，但以往工作通常假设衣物已展开就绪。在医疗场景中，防护服和围裙常以折叠状态存放，需额外展开步骤。本文首次提出“预穿衣”环节，即展开衣物的过程。我们采用模仿学习训练三种操作原语，涵盖高、低加速度动作，并引入视觉分类器识别衣物状态（封闭、部分打开、完全打开）。通过实证评估所学操作原语及其组合效果，结果表明：高速动作对刚拆封的折叠衣物无效，而动作组合可显著改善展开效果。
原文摘要 · Abstract (English)
Robotic-assisted dressing has the potential to significantly aid both patients as well as healthcare personnel, reducing the workload and improving the efficiency in clinical settings. While substantial progress has been made in robotic dressing assistance, prior works typically assume that garments are already unfolded and ready for use. However, in medical applications gowns and aprons are often stored in a folded configuration, requiring an additional unfolding step. In this paper, we introduce the pre-dressing step, the process of unfolding garments prior to assisted dressing. We leverage imitation learning for learning three manipulation primitives, including both high and low acceleration motions. In addition, we employ a visual classifier to categorise the garment state as closed, partly opened, and fully opened. We conduct an empirical evaluation of the learned manipulation primitives as well as their combinations. Our results show that highly dynamic motions are not effective for unfolding freshly unpacked garments, where the combination of motions can efficiently enhance the opening configuration.
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