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.

This paper presents MetaTwin, a collaborative Metaverse platform that supports one-to-one spatiotemporal synchrony between physical and virtual spaces. The users can interact with other users and surrounding IoT devices without being tied to physical spaces. Resource sharing is implemented to allow users to share media, including presentation slides and music. We deploy MetaTwin in two different network environments (i.e., within the US, Korea-US international) and summarize users’ feedback about the experience.