Immersive virtual environment for mobile platform remote operation and exploration
Bibliographic record
Abstract
These days, robotic platforms are commonly used in operational conditions where manned operations are not practical, not cost-effective or too dangerous. Those robotic devices rely heavily on remote operations using imagery acquired by on-board sensors that provide quite limited situational awareness to the user. In difficult scenarios, this lack of good situational awareness could lead to the failure of the mission. This paper presents a new concept currently in development that will improve situational awareness of the remote platform operator through an immersive virtual environment. The system uses an immersive chamber (CAVE) in which the operator is able to visualize and interact with an avatar of a robot evolving in a 3D model of its area of operation. The 3D model is incrementally built from the remote platform sensor feeds and provides “persistent data” to the user. This paper presents the first phase of the work which involves the development of a concept demonstration prototype. The implementation uses a robot simulator instead of a real world robot in order to rapidly be able to evaluate the concept and perform experiments. The tools developed in simulation will serve as the base for further developments and support the transition to a real robotic platform.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".