Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering
Model-mediated teleoperation frameworks collect remote texture signals and render stable, low-latency haptic texture feedback at the leader side.
Researchers: Mudassir Ibrahim Awan, Tatyana Ogay, Waseem Hassan, Dongbeom Ko, Sungjoo Kang, and Seokhee Jeon
This work presents the first model-mediated teleoperation (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. An initial proof-of-concept system supporting single and homogeneous surfaces is implemented and evaluated, demonstrating the potential of the approach.
Related Publications:
- Mudassir Ibrahim Awan, T. Ogay, W. Hassan, D. Ko, S. Kang, and Seokhee Jeon, "Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering," 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 12457-12463, doi: 10.1109/ICRA48891.2023.10160503.
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Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering
Mudassir Ibrahim Awan, Tatyana Ogay, Waseem Hassan, D. Ko, S. Kang, Seokhee Jeon
IEEE ICRA 2023, London, 2023 · Acceptance rate 43.0% (1,345/3,125)
