@Article{abderrahmane:ras:2018,
  author    = {Abderrahmane, Zineb and Ganesh, Gowrishankar and Crosnier, Andr{\'e} and Cherubini, Andrea},
  title     = {Haptic Zero-Shot Learning: Recognition of objects never touched before},
  journal   = {Robotics and Autonomous Systems},
  year      = {2018},
  volume    = {105},
  pages     = {11--25},
  month     = {July},
  doi       = {https://doi.org/10.1016/j.robot.2018.03.002},
  url       = {https://www.researchgate.net/publication/324070508\_Haptic\_Zero-Shot\_Learning\_Recognition\_of\_objects\_never\_touched\_before},
  keywords  = {Haptic recognition; Zero-Shot Learning; Attribute-based description; Robotic hand},
  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 [1] 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.},
  publisher = {Elsevier Science BV},
  address   = {Po Box 211, 1000 AE Amsterdam, Netherlands}
}