软腕具各向异性可调刚度，提升机器人接触操作鲁棒性
A Soft Wrist with Anisotropic and Selectable Stiffness for Robust Robot Learning in Contact-rich Manipulation
- 用正交弹簧+锁紧关节设计，实现大范围六自由度变形
- 三模式可调刚度，实测装配成功率76%远超对比方案
- 轻量低成本，适合精密装配与易损物操作场景
在非结构化环境中，高接触交互任务对机器人学习的鲁棒性构成重大挑战，意外碰撞易导致损坏并阻碍策略学习。现有软末端执行器存在变形范围有限、缺乏方向刚度控制或需复杂驱动系统等根本缺陷。本研究提出新型软腕机构CLAW（Compliant Leaf-spring Anisotropic soft Wrist），采用两个正交簧片与带锁紧机制的旋转关节，实现大范围6自由度变形（横向40mm，纵向20mm），具备三种可调刚度模式，整体重330g，成本仅550美元。基于模仿学习的实验表明，CLAW在基准插销任务中成功率达76%，显著优于Fin Ray夹爪（43%）和刚性夹爪（36%）。该设计能有效应对多种高接触场景，包括高精度装配与精细物体操作，展现其在接触密集型任务中实现鲁棒机器人学习的潜力。
原文摘要 · Abstract (English)
Contact-rich manipulation tasks in unstructured environments pose significant robustness challenges for robot learning, where unexpected collisions can cause damage and hinder policy acquisition. Existing soft end-effectors face fundamental limitations: they either provide a limited deformation range, lack directional stiffness control, or require complex actuation systems that compromise practicality. This study introduces CLAW (Compliant Leaf-spring Anisotropic soft Wrist), a novel soft wrist mechanism that addresses these limitations through a simple yet effective design using two orthogonal leaf springs and rotary joints with a locking mechanism. CLAW provides large 6-degree-of-freedom deformation (40mm lateral, 20mm vertical), anisotropic stiffness that is tunable across three distinct modes, while maintaining lightweight construction (330g) at low cost ($550). Experimental evaluations using imitation learning demonstrate that CLAW achieves 76% success rate in benchmark peg-insertion tasks, outperforming both the Fin Ray gripper (43%) and rigid gripper alternatives (36%). CLAW successfully handles diverse contact-rich scenarios, including precision assembly with tight tolerances and delicate object manipulation, demonstrating its potential to enable robust robot learning in contact-rich domains. Project page: https://project-page-manager.github.io/CLAW/
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