混合触觉传感器融合事件与帧数据，实现高精度长期力感知。
Mixtac: A Novel Bio-Inspired Hybrid Tactile Sensor with Synergistic Event-Frame Perception
- 模仿生物机械感受器，融合事件与帧信号进行触觉感知。
- 在静态力估计中实现0.04 N的平均绝对误差，性能优异。
- 适合需要高采样率与长期稳定性的机器人灵巧操作任务。
基于视觉和事件的触觉传感器在机器人操作研究中至关重要。然而，二者存在根本性权衡：基于视觉的传感器采样率低，而基于事件的传感器在长时间静态力估计中易产生漂移。为解决此问题并实现类人触觉感知，本文提出一种新型生物启发式混合触觉传感器（Mixtac），模拟生物机械感受器的协同功能，实现法向力估计。原型利用事件信号进行高频力追踪，利用帧信号保证长期精度。为此提出帧引导事件循环网络（FGER-Net），在训练中用帧纠正事件漂移，在推理中引导高频预测。实验表明，该系统达到0.04 N的平均绝对误差（MAE）。本工作填补了当前基于视觉的触觉传感器从0到500 Hz采样率的空白，为实现类人级机器人操作铺平道路。
原文摘要 · Abstract (English)
Vision based and event based tactile sensors are important in robotic manipulation research. However, they suffer from a fundamental tradeoff: vision based sensors have low sampling rates, while event based sensors are prone to drift during long term static force estimation. To solve this challenge and achieve human level tactile perception, the novel hybrid event frame tactile sensor (Mixtac) is proposed in this paper by emulating the synergistic function of biological mechanoreceptors, which achieves normal force estimation. The prototype leverages events for high frequency force tracking and frames for long term accuracy. The Frame Guided Event Recurrent Network (FGER-Net) was proposed to fuse the two data streams. Frames were used by the net to correct event drift during training and guide high frequency predictions during inference. Experiments demonstrated an MAE of 0.04 N. This paper could bridge the sampling rate gap from 0 to 500 Hz in current vision based tactile sensors and pave the way for human level robotic manipulation.
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