TY - GEN

T1 - Model-Mediated Teleoperation for Remote Haptic Texture Sharing

T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023

AU - Awan, Mudassir Ibrahim

AU - Ogay, Tatyana

AU - Hassan, Waseem

AU - Ko, Dongbeom

AU - Kang, Sungjoo

AU - Jeon, Seokhee

N1 - 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.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1109/ICRA48891.2023.10160503

DO - 10.1109/ICRA48891.2023.10160503

M3 - Article in proceedings

AN - SCOPUS:85168704091

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 12457

EP - 12463

BT - Proceedings - ICRA 2023

PB - IEEE

Y2 - 29 May 2023 through 2 June 2023

ER -