首个从表面肌电信号到肌腱控制的大规模数据集，助力机械手精准操控。
emg2tendon: From sEMG Signals to Tendon Control in Musculoskeletal Hands
- 构建包含193人、370小时、29种手势的sEMG到肌腱控制数据集
- 提出基于扩散模型的新方法，提升肌电信号转肌腱控制精度
- 适用于需要高灵巧度机械手控制的研究与工程应用
肌腱驱动的机械手在操作任务中表现出卓越灵巧性，但其控制策略学习面临独特挑战。与关节驱动系统不同，肌腱驱动系统缺乏运动捕捉数据与肌腱控制之间的直接一对一映射，导致学习过程复杂且成本高昂。此外，现实场景中的视觉追踪易受遮挡和误差影响，进一步增加关节追踪难度。腕部可穿戴表面肌电（sEMG）传感器提供了一种低成本、鲁棒的手部运动采集方案。然而，将sEMG信号映射至肌腱控制仍具挑战，尽管现有文献已存在肌电到姿态的数据集和回归模型。本文首次发布大规模sEMG到肌腱控制数据集emg2tendon，扩展自emg2pose数据集，涵盖193名受试者、370小时记录、29种动作阶段，其肌腱控制信号通过MyoSuite MyoHand模型生成，解决了先前方法中存在的无效姿态问题。我们提供三种基准回归模型，并提出一种新型基于扩散的回归模型，用于从sEMG信号预测肌腱控制。该数据集与建模框架为肌腱驱动灵巧机械手操控迈出重要一步，为可扩展、高精度的机械手控制奠定基础。
原文摘要 · Abstract (English)
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. https://emg2tendon.github.io/
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