Sep 17, 2026 · cs.ROJ/K move · Enter open · S save
Zeqing Zhang, Zuokun Xie, Ao Fang, Bin Dai+3
Nanyang Technological University · The University of Hong Kong · Chinese Academy of Sciences
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.