用双臂机器人自动抚平任意褶皱的布料到指定形状，精度高且适应多种布料。
RTFF: Random-to-Target Fabric Flattening Policy using Dual-Arm Manipulator
- 通过模板网格对齐布料当前与目标状态，实现像素级褶皱与姿态评估。
- 结合模仿学习与视觉伺服，先粗调后精修，实现对未见过目标的精准抚平。
- 在真实双臂系统上验证，可处理不同材质、尺寸和目标形状的布料。
机器人操控布料仍面临布料易变形及褶皱遮挡机械臂等挑战。本文定义了随机褶皱布料到任意用户指定无褶皱目标姿态的随机到目标布料抚平（RTFF）任务。该任务需同时完成抚平与姿态对齐，二者相互耦合：抚平会改变布料位置，而重新对齐又会引入新褶皱。为此，本文将当前与目标布料状态锚定于同一模板网格，实现无需配准的逐顶点褶皱与姿态评估。基于此表示，提出一种混合模仿学习-视觉伺服（IL-VS）的RTFF策略。新颖的网格动作分块变换器（MACT）利用结构化网格观测，从紧凑示范集中实现目标条件下的粗略对齐，随后视觉伺服确保精确收敛至目标。该策略在真实双臂遥操作平台上验证，展现出对未见过目标姿态、布料类型和尺度的精确对齐能力。代码与视频：https://kaitang98.github.io/RTFF_Policy/
原文摘要 · Abstract (English)
Robotic fabric manipulation remains challenging due to fabric deformability and occlusions from wrinkles and the manipulator. This paper defines Random-to-Target Fabric Flattening (RTFF) as the task of bringing a randomly wrinkled fabric to an arbitrary user-specified wrinkle-free target pose. RTFF requires simultaneous flattening and pose alignment, where the two objectives are inherently coupled since flattening the fabric displaces its pose, while realigning it tends to introduce wrinkles. To solve this task, this paper anchors both the current and target fabric states to the same template mesh, enabling direct vertex-level wrinkle and pose assessment without registration. Building on this representation, a hybrid Imitation Learning--Visual Servoing (IL--VS) RTFF policy is proposed. A novel Mesh Action Chunking Transformer (MACT) leverages structured mesh observations to achieve goal-conditioned coarse alignment from a compact demonstration set, after which VS ensures precise convergence to the target. The policy is validated on a real dual-arm teleoperation system, demonstrating precise alignment to unseen target poses, fabric types, and scales. Code and videos: https://kaitang98.github.io/RTFF_Policy/
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