CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors
This work addresses object recognition for robotics using tactile sensors, but it is incremental as it applies existing CNN methods to new sensor data.
The paper tackled object recognition using high-resolution tactile sensors by applying CNN-based methods, including transfer learning and a custom CNN (TactNet), achieving results compared to the state-of-the-art with 11 tested configurations.
Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as Convolutional Neural Networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to identify contacted objects. Two CNN-based approaches have been employed to classify pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB-images dataset and a custom-made CNN (TactNet) trained from scratch with tactile information. The transfer learning approach can be carried out by retraining the classification layers of the network or replacing these layers with an SVM. Overall, 11 configurations based on these methods have been tested: 8 transfer learning-based, and 3 TactNet-based. Moreover, a study of the performance of the methods and a comparative discussion with the current state-of-the-art on tactile object recognition is presented.