用肌电信号实现轻量级灵巧手远程操控，无需校准即可跨场景通用。
DexEMG: Towards Dexterous Teleoperation System via EMG2Pose Generalization
- 通过肌电到手势的神经网络直接预测手部运动
- 在多种任务中实现高精度操控，无需个体校准
- 适合通用机器人操作与辅助设备，部署便捷
高保真灵巧机械手的远程操控对机器人进入非结构化家庭环境至关重要。现有系统在性能与便携性间存在权衡：基于视觉的捕获系统受限于成本与视线要求，而机械外骨骼则笨重且限制行动。本文提出DexEMG，一种轻量、低成本的远程操控系统，利用表面肌电（sEMG）实现人机意图衔接。我们采集了同步的sEMG信号与手部姿态数据，通过动作捕捉手套训练出EMG2Pose神经网络，可直接从肌肉活动连续预测手部运动学。为实现无缝控制，开发了鲁棒的手部重定向算法，实时将预测姿态映射至多指灵巧手。实验表明，DexEMG在多样化任务中表现出高精度，且无需个体特定校准即可在新物体和复杂环境中良好泛化。该工作为通用机器人操作与辅助技术提供了可扩展、直观的交互接口。
原文摘要 · Abstract (English)
High-fidelity teleoperation of dexterous robotic hands is essential for bringing robots into unstructured domestic environments. However, existing teleoperation systems often face a trade-off between performance and portability: vision-based capture systems are constrained by costs and line-of-sight requirements, while mechanical exoskeletons are bulky and physically restrictive. In this paper, we present DexEMG, a lightweight and cost-effective teleoperation system leveraging surface electromyography (sEMG) to bridge the gap between human intent and robotic execution. We first collect a synchronized dataset of sEMG signals and hand poses via a MoCap glove to train EMG2Pose, a neural network capable of continuously predicting hand kinematics directly from muscle activity. To ensure seamless control, we develop a robust hand retargeting algorithm that maps the predicted poses onto a multi-fingered dexterous hand in real-time. Experimental results demonstrate that DexEMG achieves high precision in diverse teleoperation tasks. Notably, our system exhibits strong generalization capabilities across novel objects and complex environments without the need for intensive individual-specific recalibration. This work offers a scalable and intuitive interface for both general-purpose robotic manipulation and assistive technologies.
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