Transfer learning for visionbased tactile sensing Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent advances in deep neural networks applied to computer vision make visionbased tactile sensors very appealing for their highresolution and low cost. A soft optical tactile sensor that is scalable to large surfaces with arbitrary shape is discussed in this paper. A supervised learning algorithm trains a model that is able to reconstruct the normal force distribution on the sensorxe2x80x99s surface, purely from the images recorded by an internal camera. In order to reduce the training times and the need for large datasets, a calibration procedure is proposed to transfer the acquired knowledge across multiple sensors while maintaining satisfactory performance.

VisionBased Tactile Perception for Soft Robotic Hand * The innovative visionbased tactile perception for soft robotic hand is proposed in this paper. The soft finger consists of a coloured elastic inner chamber, an outer structure, an endoscope camera, and a sealing device. The camera is used to capture the images of the inner chamber for tactile perception of soft robotic hand. Firstly the design and fabrication of the soft hand is descripted. Then the algorithm of recognizing the bending posture is proposed. The color regions boundary of image is extracted, and the convolutional neural networks is deduced to estimate the bending posture. The referred points from the boundary are selected, and the bending direction and the object surfacesxe2x80x99 profile can be obtained by tracking displacement of the points. Finally, several experiments are designed and the results show the effectiveness of the proposed method and the superior performance of visionbased tactile perception.