Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear
This addresses a key challenge in robotic touch for robots using shear-sensitive sensors, offering a method to improve tactile perception, though it appears incremental as it builds on existing deep learning approaches.
The paper tackles the problem of motion-induced shear distorting tactile images in soft optical sensors, proposing a supervised convolutional neural network that disentangles contact geometry from shear to reconstruct unsheared images and enable full object reconstruction of 2D shapes.
Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the geometry of the stimulus. In this work, we propose a supervised convolutional deep neural network model that learns to disentangle, in the latent space, the components of sensor deformations caused by contact geometry from those due to sliding-induced shear. The approach is validated by reconstructing unsheared tactile images from sheared images and showing they match unsheared tactile images collected with no sliding motion. In addition, the unsheared tactile images give a faithful reconstruction of the contact geometry that is not possible from the sheared data, and robust estimation of the contact pose that can be used for servo control sliding around various 2D shapes. Finally, the contact geometry reconstruction in conjunction with servo control sliding were used for faithful full object reconstruction of various 2D shapes. The methods have broad applicability to deep learning models for robots with a shear-sensitive sense of touch.