Uncertainty Aware Texture Classification and Mapping Using Soft Tactile Sensors

Abstract

Spatial mapping of surface roughness is a critical enabling technology for automating adaptive sanding operations. We leverage GelSight sensors to convert the problem of surface roughness measurement into a vision classification problem. By combining GelSight sensors with Optitrack positioning systems we attempt to develop an accurate spatial mapping of surface roughness that can compare to human touch, the current state of the art for large scale manufacturing. To perform the classification, we propose the use of Bayesian neural networks in conjunction with uncertainty-aware prediction. We compare the sensor and network with a human baseline for both absolute and relative texture classification. To establish a baseline, we collected performance data from humans on their ability to classify materials into 60, 120, and 180 grit sanded pine boards. Our results showed that the probabilistic network performs at the level of human touch for absolute and relative classifications. Using the Bayesian approach enables establishing a confidence bound on our prediction. We were able to integrate the sensor with Optitrack to provide a spatial map of sanding grit applied to pine boards. From this result, we can conclude that GelSight with Bayesian neural networks can learn accurate representations for sanding, and could be a significant enabling technology for closed loop robotic sanding operations.