Fingertip 6-Axis Force/Torque Sensing for Texture Recognition in Robotic Manipulation

Abstract

The human sense of touch allows recognizing a wide set of properties of a grasped object such as weight, shape, hardness, temperature or surface texture. Despite the great importance of haptic sensing for humans, mechatronic end-effectors of humanoid robots and industrial manipulators are rarely endowed with tactile feedback. This is due to a lack of robust force/torque sensors which are compact enough to be integrated in the robot's fingertips. This paper leverages a novel 6-axis force/torque sensor and investigates, how local force/torque sensing at the end-effector fingertip best enables the robot to classify different surface textures. Fingertip measurements of reaction forces and torques are recorded for a total of 21 textures as the robot performs sliding movements similar to those that humans make when exploring textures. After data collection and signal processing, the extracted features are used for texture recognition, utilizing k-nearest neighbor (kNN), decision tree, random forest as well as multi-layer perceptron (MLP) classifiers. Our experimental results show that the concatenated power spectral densities extracted from the force and torque time series are the most discriminative input features enabling the random forest to achieve an average recognition accuracy of 98.8±0.4%.