用特制机械腕+分层控制，实现手绢高速旋转的精准稳定操控
Periodic Steady-State Control of a Handkerchief-Spinning Task Using a Parallel Anti-Parallelogram Tendon-driven Wrist
- 设计平行反平行四边形肌腱驱动腕，实现90度全向旋转且无耦合
- 硬件实验达成99%展开率，指尖追踪误差仅2.88毫米RMSE
- 适合研究柔性物体操控与机器人精密运动控制的团队参考
旋转柔性物体（如中国传统手绢表演）需在非线性动力学、摩擦接触和边界约束下实现周期稳态运动。为此，我们首先设计了一种基于平行反平行四边形肌腱驱动结构的灵巧腕，具备90度全向旋转能力、低惯量及解耦的滚转-俯仰感知；并采用高低层级分层控制方案。随后构建手绢的粒子-弹簧模型，用于控制建模与策略评估。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，指尖轨迹追踪均方根误差（RMSE）为2.88毫米。结果表明，将面向控制的建模与任务定制的灵巧腕结合，可实现从初始状态到稳态的鲁棒过渡及对高度柔性物体的精确周期性操控。
原文摘要 · Abstract (English)
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/
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