Active Clothing Material Perception Using Tactile Sensing and Deep Learning
Name
1711.00574.pdf
Description
Submitted version
Size
7.5 MB
Format
Adobe PDF
Checksum (MD5)
4323e800fe95e5dbf336c5e24e0a7d77
Author(s)
Yuan, Wenzhen
Mo, Yuchen
Wang, Shaoxiong
Adelson, Edward H
Date Issued
September 2018
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yuan, Wenzhen et al. "Active Clothing Material Perception Using Tactile Sensing and Deep Learning." IEEE International Conference on Robotics and Automation (ICRA), May 2018, Brisbane, Australia, Institute of Electrical and Electronics Engineers, September 2018. © 2018 IEEE
Version
Original manuscript
Abstract
Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.
MIT Department
Massachusetts Institute of Technology. Laboratory for Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8461164
