Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  • Mudassir Ibrahim Awan
  • Tatyana Ogay
  • Hassan, Waseem
  • Dongbeom Ko
  • Sungjoo Kang
  • Seokhee Jeon

While model-mediated teleoperation (MMT) is an effective alternative for ensuring both transparency and stability, its potential in transmitting surface haptic texture is not yet explored. This paper introduces the first MMT framework capable of sharing surface haptic texture. The follower side collects physical signals contributing to haptic texture perception, e.g., high frequency acceleration, and streams them to the leader side. The leader side uses the signals to build and update a local measurement-based texture simulation model that reflects the remote surface. At the same time, the leader runs local simulation using the model, resulting in non-delayed, stable, and accurate feedback of texture. Considering that rendering haptic texture needs tougher real-time requirements, e.g., higher update rate and lower action-feedback latency, MMT can be a perfect platform for remote texture sharing. An initial proof-of-concept system supporting single and homogeneous surface is implemented and evaluated, demonstrating the potential of the approach.

Original languageEnglish
Title of host publicationProceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation
Number of pages7
PublisherIEEE
Publication date2023
Pages12457-12463
ISBN (Electronic)9798350323658
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
LandUnited Kingdom
ByLondon
Periode29/05/202302/06/2023
SeriesProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN1050-4729

Bibliographical note

Funding Information:
This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government. [23ZS1300, Research on High Performance Computing Technology to overcome limitations of AI processing]. The authors would like to extend their gratitude towards Heather Culbertson et al. for sharing their haptic texture rendering code online. The authors are also grateful towards Arsen Abdulali et al. for providing their texture segmentation framework.

Publisher Copyright:
© 2023 IEEE.

ID: 388954720