DexTwist：基于混合现实的灵巧手扭动动作重定向
DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation
AI总结：
本文提出DexTwist框架，通过混合现实接口实现灵巧手扭动动作的重定向，解决传统方法在接触丰富旋转操作中的不足，提升扭动角度跟踪和螺轴稳定性。
摘要：
基于混合现实（MR）的灵巧远程操控提供了一种可扩展的范式，用于将人类操控技能转移到灵巧机器人手中。然而，传统重定向方法通过最小化运动学差异（如关节角度或指尖位置误差）往往在接触丰富的旋转操作中失效，如开启杯盖、转动钥匙和拧紧螺丝。这种失效源于身体差距：不匹配的链接长度、关节轴/限制和指尖几何结构会导致直接姿态模仿引起指尖滑动而非稳定物体旋转，导致螺轴漂移、接触滑动和抓取不稳定。为此，我们提出了DexTwist，一种针对MR基于灵巧远程操控的函数扭动重定向框架。DexTwist检测三脚夹握，估计操作员的预期螺轴和扭动幅度，并应用实时残差关节空间细化，跟踪转动进度并正则化机器人三脚几何结构。细化最小化一个虚拟对象目标，由转动角度、螺轴一致性、指尖闭合和三脚稳定性定义。模拟和现实世界实验表明，DexTwist相比基于向量的重定向基线提高了转动角度跟踪和螺轴稳定性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
