Data and model hybrid-driven virtual reality robot operating system
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
To realize efficient remote human-computer interaction of robots, a robot remote operating system based on virtual reality and digital twin is proposed. The system builds a digital twin model based on the Unity 3D engine to establish a connection with the robot entity, assisting the online remote programming and real-time manipulation of the robot unit. The system uses HTC VIVE to build a virtual reality framework. To actualize the mutual drive between the real space and the virtual space, a mathematical model of the robot is constructed through the forward and inverse kinematics of the robot. Through the combination of eye-tracking-based eye movement interaction and the unique controller interaction of virtual reality system, a multi-sensory multi-input collaborative interaction method is accomplished. The method realizes the robot joints driving of users using multiple interaction methods simultaneously, simplifies the robot programming and control procedure, and optimizes the operation experience. Tests demonstrate that the system is capable of effectively providing monitoring, teleoperation and programming services for remote interaction of robots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".