Gamified Virtual Reality Workflow for Deconstruction Cutting and Packing Planning
Bibliographic record
Abstract
In this paper, a novel approach to deconstruction planning based on reality capture and gamified virtual reality (VR) is presented and demonstrated. In the approach, the in situ conditions of the asset are captured and stored in a 3D point cloud. Next, a mesh or BIM representation of the asset is created and imported into a 3D game engine. Finally, gamified VR software simulations are developed allowing an operator to conduct human-machine collaborative deconstruction planning experiments. As a result, deconstruction plans benefit from realistic, repeatable, risk-free iterative improvements. This approach builds upon prior work by contributing the new ability to import reality data into the deconstruction simulations. One essential disassembly workflow, cutting and packing, is fully implemented for demonstration. New, low-cost, reality-capture tools such as smartphone lidars and handheld scanners promise to diminish the cost of implementing the approach, provided that they meet the project’s needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".