Re-using prior tactile experience by robotic hands to discriminate in-hand objects via texture properties

摘要

This paper proposes an online tactile transfer learning strategy for discriminating objects through the surface texture properties via a robotic hand and an artificial robotic skin. The proposed method has the ability to autonomously select and exploit the previously learned multiple texture models while discriminating new textures with a very few available training samples or even one. The experimental results show that employing the proposed method and 10 prior texture models, the robotic hand could discriminate 12 objects via their surface textures with 97% and 100% recognition accuracy with only one and ten training samples respectively. Moreover, the experimental outcomes illustrate that our proposed algorithm is robust against of any negative tactile knowledge transfer.