Cross-Sensor Touch Generation
This work addresses a domain-specific problem for robotics and tactile sensing, offering incremental improvements in sensor translation flexibility.
The paper tackles the challenge of developing general-purpose tactile representations across different visuo-tactile sensors by proposing two cross-sensor image generation methods, Touch2Touch and T2D2, which enable transferring models trained on one sensor to another for tasks like in-hand pose estimation and behavior cloning.
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.