Variable Stiffness Object Recognition with a CNN-Bayes Classifier on a Soft Gripper

Soft grippers significantly widen the palpation capabilities of robots, ranging from soft to hard materials without the assistance of cameras. From a medical perspective, the detection of size and shape of hard inclusions concealed within soft three-dimensional (3D) objects is meaningful for the early detection of cancer through palpation. This article proposes a framework for variable-stiffness object recognition using tactile information collected by force sensitive resistors on a three-finger soft gripper. A 15 × 50 spatiotemporal tactile image is generated for each 3D palpation process and then fed into a convolutional neural network (CNN) for object identification. The training set consists of tactile images generated from different grasping orientations. We developed our own CNN architecture, named SoftTactNet, and compared its performance with several state-of-the-art CNNs on the image dataset produced by our experiments. The results show that our proposed method excels in distinguishing 3D shapes and sizes of objects enclosed by a thick soft foam. The average recognition rate is significantly improved using a Naive Bayes classifier, reaching a 97% recognition accuracy. The detection of shapes and sizes of hard objects underneath soft tissues is extremely important for breast and testicular cancer early detection, a field where Soft Robots can shine with inexpensive and ubiquitous devices.