Abstract
Tendon-driven robotic hands offer unparalleled dexterity for manipulation tasks, but learning control policies for such systems presents unique challenges. Unlike joint-actuated robotic hands, tendon-driven systems lack a direct one-to-one mapping between motion capture (mocap) data and tendon controls, making the learning process complex and expensive. Additionally, visual tracking methods for real-world applications are prone to occlusions and inaccuracies, further complicating joint tracking. Wrist-wearable surface electromyography (sEMG) sensors present an inexpensive, robust alternative to capture hand motion. However, mapping sEMG signals to tendon control remains a significant challenge despite the availability of EMG-to-pose data sets and regression-based models in the existing literature. We introduce the first large-scale EMG-to-Tendon Control dataset for robotic hands, extending the emg2pose dataset, which includes recordings from 193 subjects, spanning 370 hours and 29 stages with diverse gestures. This dataset incorporates tendon control signals derived using the MyoSuite MyoHand model, addressing limitations such as invalid poses in prior methods. We provide three baseline regression models to demonstrate emg2tendon utility and propose a novel diffusion-based regression model for predicting tendon control from sEMG recordings. This dataset and modeling framework marks a significant step forward for tendon-driven dexterous robotic manipulation, laying the groundwork for scalable and accurate tendon control in robotic hands. this https URL
Abstract (translated)
肌腱驱动的机器人手在操控任务中提供了无与伦比的灵巧性，但为其系统学习控制策略却面临着独特的挑战。与关节驱动的机器人手不同，肌腱驱动系统缺乏运动捕捉（mocap）数据和肌腱控制之间的一对一映射关系，使得学习过程变得复杂且成本高昂。此外，用于实际应用中的视觉跟踪方法易受遮挡和不准确的影响，进一步增加了关节追踪的难度。手腕穿戴式的表面肌电图（sEMG）传感器提供了一种低成本且稳健的方式来捕捉手部动作，然而将sEMG信号映射到肌腱控制仍然是一项重大挑战，尽管现有的文献中已有EMG至姿态数据集和基于回归模型的方法。 我们首次推出了大规模的EMG至肌腱控制数据集，适用于机器人手，并扩展了emg2pose数据集，该数据集收录了193名受试者的记录，总计时长为370小时，涵盖了29个不同手势阶段。此数据集包含通过MyoSuite MyoHand模型导出的肌腱控制信号，克服了先前方法中存在的无效姿态等问题。我们提供了三种基础回归模型来展示emg2tendon工具的有效性，并提出了一种基于扩散的新颖回归模型，用于从sEMG记录中预测肌腱控制。这一数据集和建模框架标志着肌腱驱动的灵巧机器人操控技术向前迈出的一大步，为在机器人手中实现可扩展且准确的肌腱控制奠定了基础。 相关链接：[请在此处插入实际网址](https://this.url)
URL
https://arxiv.org/abs/2508.08269
