Periodic Steady-State Control of a Handkerchief-Spinning Task Using a Parallel Anti-Parallelogram Tendon-Driven Wrist
Andrew Ross McIntosh, Kai Sun,, Zhenshan Bing,, Jiahong Dong,,, Fuchun Sun
AI summary
Problem
Dynamic manipulation of highly flexible objects remains challenging due to nonlinear dynamics, frictional contacts, and the lack of dexterous, low-inertia robotic wrists capable of sustaining periodic steady-state motions.
Approach
The authors design a compact parallel anti-parallelogram tendon-driven wrist with decoupled sensing and implement a two-level hierarchical control scheme, supported by a particle-spring simulation model to evaluate and optimize initiation strategies.
Key results
- Designed a parallel anti-parallelogram tendon-driven wrist with 90° omnidirectional rotation and low inertia
- Developed a high-low level hierarchical control scheme for robust rest-to-steady-state transitions
- Created a control-oriented particle-spring model for simulating fabric dynamics and strategy evaluation
- Achieved ~99% unfolding ratio and 2.88 mm RMSE tracking error in hardware experiments
Why it matters
Provides a practical hardware-control framework for dynamic deformable object manipulation, advancing robotics applications requiring precise handling of highly flexible materials.
Abstract
Spinning flexible objects, exemplified by tradi- tional 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 paral- lel anti-parallelogram tendon-driven structure, which achieves 90◦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 manipula- tion of highly flexible objects. More visualizations: https:// slowly1113.github.io/icra2026-handkerchief/.