GraspLook: a VR-based Telemanipulation System with R-CNN-driven Augmentation of Virtual Environment
Polina Ponomareva, Daria Trinitatova, Aleksey Fedoseev, Ivan Kalinov,, Dzmitry Tsetserukou

TL;DR
This paper introduces GraspLook, a VR telemanipulation system enhanced with R-CNN for detecting instruments and augmenting virtual environments, improving operation smoothness and reducing mental load in remote laboratory tasks.
Contribution
It presents a novel VR-based teleoperation system that uses R-CNN for real-time instrument detection and virtual environment augmentation, enhancing remote manipulation efficiency.
Findings
System reduces task execution time
Participants find system less mentally demanding
High user satisfaction with augmented virtual environment
Abstract
The teleoperation of robotic systems in medical applications requires stable and convenient visual feedback for the operator. The most accessible approach to delivering visual information from the remote area is using cameras to transmit a video stream from the environment. However, such systems are sensitive to the camera resolution, limited viewpoints, and cluttered environment bringing additional mental demands to the human operator. The paper proposes a novel system of teleoperation based on an augmented virtual environment (VE). The region-based convolutional neural network (R-CNN) is applied to detect the laboratory instrument and estimate its position in the remote environment to display further its digital twin in the VE, which is necessary for dexterous telemanipulation. The experimental results revealed that the developed system allows users to operate the robot smoother,…
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