分布型触覚センサ搭載の多指ハンドに対するCNNを用いた物体認識手法の提案

抄録

Distributed tactile sensing with multi-fingered hands can provide high-dimensional information for grasping objects but it is not clear how to optimally process such abundant tactile information. The current paper explores the possibility of using a morphology-specific convolutional neural network (MS-CNN). In particular, uSkin tactile sensors are mounted on an Allegro Hand, which provide 720 force measurements in addition to 16 joint angle measurements. Consecutive layers in the CNN get input from parts of one finger segment, one finger, and the whole hand. Since the sensors give 3D (x, y, z) vector tactile information, inputs with 3 channels (x, y and z) are used in the first layer, derived from the idea of such inputs for RGB images from cameras. Overall, the layers are combined resulting in building a tactile map based on the relative position of the tactile sensors on the hand. 7 combining variations were evaluated, and over 95 % object recognition rate with 20 objects was achieved, even though only one random time instance from a repeated squeezing motion of an object in an unknown pose within the hand was used as input.