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/

翻译：非结构化环境中的接触密集型操作任务对机器人学习提出了显著的鲁棒性挑战，意外碰撞可能导致损坏并阻碍策略习得。现有柔性末端执行器面临根本性局限：它们要么提供有限的变形范围，缺乏定向刚度控制，要么需要复杂的驱动系统而损害实用性。本研究提出CLAW（柔性板簧各向异性软腕），这是一种新型柔性手腕机构，通过采用两个正交板簧和带锁定机制的旋转关节的简洁高效设计，解决了上述局限。CLAW提供大幅六自由度变形（横向40毫米，垂直20毫米），具备可在三种不同模式下调节的各向异性刚度，同时保持轻量化结构（330克）与低成本（550美元）。使用模仿学习的实验评估表明，CLAW在基准插销任务中达到76%的成功率，优于Fin Ray夹爪（43%）和刚性夹爪替代方案（36%）。CLAW成功处理了多样化的接触密集型场景，包括精密装配与微小公差操作以及精细物体操控，证明了其在接触密集型领域实现鲁棒机器人学习的潜力。项目页面：https://project-page-manager.github.io/CLAW/