用事件相机实现高精度三维力场重建，助力机器人灵巧操作
Dense Force Estimation with an Event-based Optical Tactile Sensor
- 通过事件数据估计表面位移，结合逆有限元法推导力分布
- 在4N、4N、20N力范围内误差为0.14/0.10/0.93N，稳定运行于100Hz
- 首次实现事件触觉传感器的密集三维力场重建，适合高速抓握控制
人类依赖高时空分辨率、稠密且兼具几何与受力感知的触觉反馈来完成灵巧操作。尽管基于视觉的触觉传感器可实现稠密力估计，但受限于摄像头帧率、运动模糊和数据带宽。事件式光学触觉传感器具备微秒级时间分辨率和低运动模糊，但现有方法仅能预测总力。本文提出首个基于事件式光学触觉传感器的密集三维力场重建框架。通过事件数据估计三维表面位移，并利用逆有限元法（iFEM）映射至力；剪切位移由提出的事件标记追踪算法恢复，法向位移则由在同步力-位移-事件数据集上训练的卷积神经网络预测。实验表明，该方法能准确重构物理合理的力场，在力范围达(4 N, 4 N, 20 N)时，平均绝对误差分别为(0.14 N, 0.10 N, 0.93 N)，且平均运行频率为100 Hz。本工作为实现机器人抓取与灵巧操作中的高频率稠密力反馈迈出关键一步。
原文摘要 · Abstract (English)
Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting only net forces. We introduce the first framework for dense 3D force field reconstruction using event-based optical tactile sensors. Our approach estimates 3D surface displacements from event data and maps them to forces via the inverse Finite Elements Method (iFEM). Shear displacements are recovered through the proposed event-based marker tracking algorithm, while normal displacements are predicted by a convolutional neural network trained on a collected dataset of synchronized force-displacement-event data. Experiments demonstrate accurate reconstruction of physically grounded forces, achieving a mean absolute error of (0.14 N, 0.10 N, 0.93 N) over force ranges up to (4 N, 4 N, 20 N), while operating at an average of 100 Hz. This work constitutes a first step toward enabling dense force feedback for high-frequency control in robotic grasping and dexterous manipulation.
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