跨传感器生成触觉图像，实现不同触觉设备间模型迁移。
Cross-Sensor Touch Generation
- 通过配对数据或中间深度表征，实现跨传感器触觉图像生成。
- 在无配对数据情况下仍可完成触觉特征转换，支持模型迁移。
- 适用于触觉感知、物体姿态估计等任务，适合多传感器场景应用。
当前视觉-触觉传感器形态多样，导致通用触觉表征难以构建，因多数模型依赖特定传感器设计。为解决此问题，本文提出两种跨传感器触觉图像生成方法：一是基于配对数据的端到端方法（Touch2Touch）；二是不需配对数据的中间深度表征方法（T2D2：Touch-to-Depth-to-Touch）。两者均通过跨传感器触觉生成，使特定传感器训练的模型可迁移至其他传感器。我们在手部姿态估计和行为克隆等下游任务中验证了其有效性，成功将模型从一个传感器迁移到另一个传感器。项目页面：https://samantabelen.github.io/cross_sensor_touch_generation。
原文摘要 · Abstract (English)
Today's visuo-tactile sensors come in many shapes and sizes, making it challenging to develop general-purpose tactile representations. This is because most models are tied to a specific sensor design. To address this challenge, we propose two approaches to cross-sensor image generation. The first is an end-to-end method that leverages paired data (Touch2Touch). The second method builds an intermediate depth representation and does not require paired data (T2D2: Touch-to-Depth-to-Touch). Both methods enable the use of sensor-specific models across multiple sensors via the cross-sensor touch generation process. Together, these models offer flexible solutions for sensor translation, depending on data availability and application needs. We demonstrate their effectiveness on downstream tasks such as in-hand pose estimation and behavior cloning, successfully transferring models trained on one sensor to another. Project page: https://samantabelen.github.io/cross_sensor_touch_generation.
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