Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin

Mohsen Kaboli, Di Feng, Gordon Cheng

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge while discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its most relevant prior tactile knowledge to efficiently learn about new unknown objects with a few training samples or even one. The experimental results show that using our proposed method, the robot successfully discriminated among new objects with 72% discrimination accuracy using only one training sample (on-shot-tactile-learning). Furthermore, the results demonstrate that our method is robust against transferring irrelevant prior tactile knowledge (negative tactile knowledge transfer).

Original languageEnglish
Article number1850001
JournalInternational Journal of Humanoid Robotics
Volume15
Issue number1
DOIs
StatePublished - 1 Feb 2018

Keywords

  • Active tactile exploration
  • active tactile transfer learning
  • active workspace exploration
  • multimodal robotic skin
  • pre-touch
  • tactile sensing

Fingerprint

Dive into the research topics of 'Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin'. Together they form a unique fingerprint.

Cite this