Sensor-Invariant Tactile Representation
Harsh Gupta Yuchen Mo Shengmiao Jin Wenzhen Yuan
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.
Tactile sensing, representation learning
We propose a representation to perform zero-shot transfer across vision-based tactile sensors