# From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor

## TL;DR

This paper demonstrates that deep learning applied to an optical biomimetic tactile sensor enables highly robust edge perception and contour following on irregular objects, even with limited training data, advancing tactile sensing in robotics.

## Contribution

It introduces a deep CNN approach for tactile edge perception using the TacTip sensor, showing robustness and generalization beyond training conditions.

## Key findings

- Deep CNN achieves reliable edge perception.
- Robust contour following on irregular objects.
- Generalization from simple to complex tasks.

## Abstract

Deep learning has the potential to have the impact on robot touch that it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this paper, we apply deep learning to an optical biomimetic tactile sensor, the TacTip, which images an array of papillae (pins) inside its sensing surface analogous to structures within human skin. Our main result is that the application of a deep CNN can give reliable edge perception and thus a robust policy for planning contact points to move around object contours. Robustness is demonstrated over several irregular and compliant objects with both tapping and continuous sliding, using a model trained only by tapping onto a disk. These results relied on using techniques to encourage generalization to tasks beyond which the model was trained. We expect this is a generic problem in practical applications of tactile sensing that deep learning will solve. A video demonstrating the approach can be found at https://www.youtube.com/watch?v=QHrGsG9AHts