让触觉传感器输出一致，实现跨设备零样本迁移。
Sensor-Invariant Tactile Representation
- 用Transformer模型在仿真数据上训练，提取通用触觉特征。
- 实测可在多种真实触觉传感器间实现零样本迁移，无需校准。
- 适合做触觉感知的科研与机器人开发人员使用。
高分辨率触觉传感器在具身感知和机器人操作中日益关键。然而，由于设计与制造差异导致的触觉信号差异，使模型在不同传感器间难以迁移。为此，我们提出一种新型方法——传感器无关触觉表征（SITR），实现光学触觉传感器间的零样本迁移。该方法基于变压器架构，在多样化的仿真传感器设计数据集上训练，可泛化至真实世界的新传感器，仅需极少校准。实验表明，该方法在多种触觉感知应用中有效，推动了未来该领域数据与模型的可迁移性发展。
原文摘要 · Abstract (English)
High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge learned from one sensor to another. To address this, we introduce a novel method for extracting Sensor-Invariant Tactile Representations (SITR), enabling zero-shot transfer across optical tactile sensors. Our approach utilizes a transformer-based architecture trained on a diverse dataset of simulated sensor designs, allowing it to generalize to new sensors in the real world with minimal calibration. Experimental results demonstrate the method's effectiveness across various tactile sensing applications, facilitating data and model transferability for future advancements in the field.
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