Sim-to-real transfer for optical tactile sensing
File(s)
Accepted version
OA Location
Author(s)
Ding, Zihan
Lepora, Nathan
Johns, Edward
Type
Conference Paper
Abstract
Deep learning and reinforcement learning meth-ods have been shown to enable learning of flexible and complexrobot controllers. However, the reliance on large amounts oftraining data often requires data collection to be carried outin simulation, with a number of sim-to-real transfer methodsbeing developed in recent years. In this paper, we study thesetechniques for tactile sensing using the TacTip optical tactilesensor, which consists of a deformable tip with a cameraobserving the positions of pins inside this tip. We designeda model for soft body simulation which was implemented usingthe Unity physics engine, and trained a neural network topredict the locations and angles of edges when in contact withthe sensor. Using domain randomisation techniques for sim-to-real transfer, we show how this framework can be used toaccurately predict edges with less than 1 mm prediction errorin real-world testing, without any real-world data at all.
Date Issued
2020-09-15
Date Acceptance
2020-01-22
Citation
IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation, 2020, pp.1639-1645
ISSN
2152-4092
Publisher
IEEE
Start Page
1639
End Page
1645
Journal / Book Title
IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Royal Academy of Engineering
Identifier
https://ieeexplore.ieee.org/abstract/document/9197512
Source
IEEE International Conference on Robotics and Automation
Publication Status
Published
Start Date
2020-05-31
Finish Date
2020-08-31
Coverage Spatial
Paris, France
Date Publish Online
2020-09-15