Embedded real-time objects’ hardness classification for robotic grippers

Abstract

Robotic grippers can be equipped with tactile sensing systems to extract information from a manipulated object. The real-time classification of the physical properties of a grasped object on resource-constrained devices requires efficient and effective pre-processing techniques and machine-learning (ML) algorithms. In this paper, we propose a tactile sensing system mounted on the Baxter robot for the hardness classification of objects. In particular, we pre-processed the raw data with low computational cost techniques, and we designed three ML algorithms to provide real-time, energy-efficient, and low-memory impact classification on a resource-constrained microcontroller. Results show that convolutional neural networks (CNNs) achieve the best accuracy (>98%), while the support vector machine (SVM) presents the lowest memory occupation (1576 bytes), inference time (<0.077ms), and energy consumption (<5.74μJ).