Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady-state motions under nonlinear dynamics with frictional contacts and boundary constraints. To address these challenges, we first design an intuitive dexterous wrist based on a parallel anti-parallelogram tendon-driven structure, which achieves 90 degrees omnidirectional rotation with low inertia and decoupled roll-pitch sensing, and implement a high-low level hierarchical control scheme. We then develop a particle-spring model of the handkerchief for control-oriented abstraction and strategy evaluation. Hardware experiments validate this framework, achieving an unfolding ratio of approximately 99% and fingertip tracking error of RMSE = 2.88 mm in high-dynamic spinning. These results demonstrate that integrating control-oriented modeling with a task-tailored dexterous wrist enables robust rest-to-steady-state transitions and precise periodic manipulation of highly flexible objects. More visualizations: https://slowly1113.github.io/icra2026-handkerchief/

翻译：旋转柔性物体，以中国传统手帕表演为例，需要在非线性动力学、摩擦接触和边界约束条件下实现周期稳态运动。为解决这些挑战，我们首先设计了一种基于平行反平行四边形腱驱动结构的直观灵巧手腕，该手腕在低惯性下实现90度全向旋转并具备解耦的横滚-俯仰感知能力，同时实现高低层级分层控制方案。随后，我们开发了手帕的粒子-弹簧模型，用于面向控制的抽象与策略评估。硬件实验验证了该框架的有效性，在高动态旋转中实现了约99%的展开率与RMSE=2.88 mm的指尖跟踪误差。这些结果表明，将面向控制的建模与任务定制灵巧手腕相结合，能够实现柔性物体从静止到稳态的鲁棒过渡与精确周期操控。更多可视化内容：https://slowly1113.github.io/icra2026-handkerchief/