RotateIt!：通过在线自适应动态旋转实现快速可靠的单臂衣物展开

中文摘要 AI 辅助

机器人衣物展开对于下游任务至关重要，然而准静态方法需要重复动作，而现有的动态方法主要依赖双臂抛掷。我们提出了RotateIt!，一个使用自适应轴向旋转进行动态衣物展开的单臂框架。据我们所知，这是第一个将动态轴向旋转作为主要操作原语的展开框架。从随机初始化的桌面配置开始，机器人选择一个旋转有效的抓取点，并围绕近似固定的锚点旋转提起的衣物，产生惯性张力，在紧凑的工作空间内分离重叠层。一个抓取排序器选择锚点，而一个在线残差策略调整旋转程度和速度，从而确定释放时机。在已见和未见过的模拟衣物以及八件未见过的真实衣物上，RotateIt!在三次尝试内的成功率比准静态拾放提高了44.0-61.0个百分点。模拟训练的策略零样本迁移到现实世界，实现了75.6%的成功率，首次尝试覆盖率提高41%，最终覆盖率提高26%。所得状态进一步实现了无需手动整理的自主机器人折叠。

英文摘要

Robotic garment unfolding is essential for downstream tasks, yet quasi-static methods require repeated actions, while existing dynamic approaches predominantly rely on bimanual flinging. We present RotateIt!, a single-arm framework that uses adaptive axial rotation for dynamic garment unfolding. To the best of our knowledge, it is the first unfolding framework to employ dynamic axial rotation as its primary manipulation primitive. From a randomly initialized tabletop configuration, the robot selects a rotation-effective grasp and rotates the lifted garment about an approximately fixed anchor, generating inertial tension that separates overlapping layers within a compact workspace. A grasp ranker selects the anchor, while an online residual policy adapts the rotation extent and speed, thereby determining the release timing. Across seen and unseen simulated garments and eight unseen real garments, RotateIt! improves success within three attempts by 44.0-61.0 percentage points over quasi-static pick-and-place. The simulation-trained policies transfer zero-shot to the real world, achieving 75.6% success, 41% higher first-attempt coverage, and 26% higher final coverage. The resulting states further enable autonomous robotic folding without manual rearrangement.

发表机构

• Nanyang Technological University（南洋理工大学）
• The University of Hong Kong（香港大学）
• Chinese Academy of Sciences（中国科学院）

机构由 AI 辅助整理，请以论文原文为准。

相关深度报道

• 单臂机器人把衣服转开再折好，实机零样本成功率75.6%