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

被引：38

作者：

Kaboli, Mohsen ^{[1
]}

Feng, Di ^{[1
]}

Cheng, Gordon ^{[1
]}

机构：

[1] Tech Univ Munich, Inst Cognit Syst, Arcisstr 21, D-80333 Munich, Germany

来源：

INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS | 2018年 / 15卷 / 01期

关键词：

Active tactile exploration; active tactile transfer learning; active workspace exploration; pre-touch; tactile sensing; multimodal robotic skin; IDENTIFICATION; CLASSIFICATION; SURFACES; TOUCH; SHAPE;

D O I：

10.1142/S0219843618500019

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

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).

引用

页数：28