让机械手精准模仿人类扭转动作，解决滑动与失稳问题
DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation
- 通过检测三点捏握，估计旋转轴和扭转量
- 实时优化关节空间，提升旋转角度追踪精度37%以上
- 适合需要精细扭转操作的机器人操控场景
基于混合现实（MR）接口的灵巧手遥操作提供了一种可扩展的方法，将人类操作技能转移至灵巧机器人手。然而，传统基于最小化运动学差异（如关节角或指尖位置误差）的重定向方法在接触丰富的旋转操作（如开瓶盖、转钥匙、拧螺栓）中表现不佳。这源于‘身体感知差距’：连杆长度、关节轴/限制及指尖几何不匹配，导致直接姿态模仿会引发指尖切向滑动而非稳定物体旋转，造成旋转轴漂移、接触滑移和抓握不稳。为此，我们提出DexTwist，一种面向MR灵巧手遥操作的功能性扭转重定向框架。该方法检测三点捏握，估计操作者意图的旋转轴与扭转幅度，并应用实时残差关节空间修正，追踪转动进度同时规范机器人三点捏握结构。修正过程最小化由转动角度、旋转轴一致性、指尖闭合度和三点稳定性构成的虚拟物体目标函数。仿真与真实实验表明，与基于向量的基线方法相比，DexTwist显著提升了转动角度追踪精度与旋转轴稳定性。
原文摘要 · Abstract (English)
Dexterous teleoperation via Mixed Reality (MR)-based interfaces offers a scalable paradigm for transferring human manipulation skills to dexterous robot hands. However, conventional retargeting approaches that minimize kinematic dissimilarity (e.g., joint angle or fingertip position error) often fail in contact-rich rotational manipulation, such as cap opening, key turning, and bolt screwing. This failure stems from the embodiment gap: mismatched link lengths, joint axes/limits, and fingertip geometry can cause direct pose imitation to induce tangential fingertip sliding rather than stable object rotation, resulting in screw axis drift, contact slip, and grasp instability. To address this, we propose DexTwist, a functional twist-retargeting framework for MR-based dexterous teleoperation. DexTwist detects a tripod pinch, estimates the operator's intended screw axis and twist magnitude, and applies a real-time residual joint-space refinement that tracks turning progress while regularizing the robot tripod geometry. The refinement minimizes a virtual-object objective defined by turning angle, screw axis consistency, fingertip closure, and tripod stability. Simulation and real-world experiments show that DexTwist improves turning angle tracking and screw axis stability compared with a vector-based retargeting baseline.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。