用肌电手环捕捉发力动作，让机器人学会精准控制抓取力度。
ForceBand: Learning Forceful Manipulation with sEMG
- 用腕戴式肌电传感器+惯性数据预测手指受力
- 力预测误差比视觉方法低50%以上，抓取成功率87%
- 只需简单校准，普通人就能快速生成带力信息的示范
人类示范是学习机器人操作策略的可扩展数据来源。然而，常见的示范数据如动作捕捉轨迹和网络视频，主要记录运动与外观，忽略了对力敏感操作至关重要的接触力。本文提出ForceBand，一种低成本腕戴式表面肌电（sEMG）系统，将人体肌肉活动转化为富含力信息的示范数据。我们构建了一个包含10小时多模态数据的数据库，涵盖第一人称视频、sEMG、IMU及指尖力测量，覆盖多种动作与物体。基于该数据集，预训练了EMG2Force模型，从sEMG和IMU信号中预测各手指受力。经过简短用户个性化校准后，用户仅需佩戴ForceBand并录制视频即可完成目标任务示范；EMG2Force自动为示范标注指尖力轨迹，生成可用于机器人策略学习的力增强示范。实验表明，ForceBand在细粒度指尖交互恢复上，力预测误差低于视觉基线50%以上，在需物体特异性力控的抓取、挤压、放置任务中达到87%成功率，适用于形状、大小、重量各异的物体。
原文摘要 · Abstract (English)
Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as motion-capture trajectories and internet videos, capture mostly motion and appearance while missing the contact forces that are critical for force-sensitive manipulation. In this paper, we introduce ForceBand, a low-cost wrist-worn sEMG system that turns human muscle activity into force-enriched demonstrations. We first collect a 10-hour multimodal dataset containing egocentric video, sEMG, IMU, and fingertip force measurements across diverse actions and objects. Using this dataset, we pre-train an EMG2Force model that predicts per-finger forces from sEMG and IMU signals. After a short user-specific calibration, users can collect target-task demonstrations using only ForceBand and video; EMG2Force then labels these demonstrations with per-finger force traces, producing force-augmented demonstrations for robot policy learning. Experiments show that ForceBand recovers fine-grained fingertip interactions with over 50% lower force prediction error than vision-based baselines and achieves an 87% success rate on pick, squeeze, and place tasks that require object-specific force control across objects with diverse shapes, sizes, and weights. Project website: https://forceband-emg.github.io
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