Variable Stiffness Object Recognition with Bayesian Convolutional Neural Network on a Soft Gripper

Abstract

From a medical standpoint, detecting the size and shape of hard inclusions hidden in soft three-dimensional objects is of great significance for early detection of cancer through palpation. Soft robots, especially soft grippers, substantially broaden robots' palpation capabilities from soft to hard materials without the assistance of a camera. We have recently introduced a CNN-Bayes approach which added a Naïve Bayes classifier to a convolutional neural network (CNN) architecture called SoftTactNet for variable stiffness object recognition on a three-finger FinRay soft gripper. SoftTactNet itself lacks uncertainty estimations though it can reach a certain level of recognition accuracy. In this paper, we further improve the framework by merging Bayes method directly into CNN architectures and build a new Bayes-SoftTactNet for object recognition. The new approach, using a prior distribution instead of point estimation, allows the network to present results with uncertainty estimates. We conduct new experiments using the same soft gripper with tactile sensor arrays to grasp different variable stiffness objects surrounded by non-different soft material and generate tactile images as dataset. The results show that our new algorithm is more efficient than the previous approach and still able to achieve higher recognition accuracy than general deterministic CNNs.