Haptic Zero-Shot Learning: Recognition of objects never touched before

Abstract

Object recognition is essential to enable robots to interact with their environment. Robots should be capable, on one hand of recognizing previously experienced objects, and on the other, of using the experienced objects for learning novel objects, i.e. objects for which training data are not available. Recognition of such novel objects can be achieved with Zero-Shot Learning (ZSL). In this work, we show the potential of ZSL for haptic recognition. First, we design a zero-shot haptic recognition algorithm and, using the extensive PHAC-2 database (Chu et al., 2015) as well as our own, we adapt, analyze and optimize the ZSL for the challenges and constraints characteristic of haptic recognition. Finally, we apply the optimized algorithm for haptic recognition of daily-life objects using an anthropomorphic robot hand. Our algorithm enables the robot to recognize eight of the ten novel objects handed to it.