Neural Network Mapping and Clustering of Elastic Behavior from Tactile and Range Imaging for Virtualized Reality Applications
Bibliographic record
Abstract
Abstract—To fully reach its potential, virtualized reality needs to go beyond the modeling of rigid bodies and introduce accurate representations of deformable objects. This paper explores neural networks and vision-based and tactile measurement strategies to investigate the intricate processes of acquisition and mapping of properties characterizing deformable objects. An original com-posite neural network framework is applied to guide the tactile probing by clustering measurements representing uniform elastic-ity regions and, therefore, direct sensors toward areas of elasticity transitions where higher sampling density is required. The net-work characterizes the relationship between surface deformation and forces that are exemplified in nonrigid bodies. Beyond serving as a planner for the acquisition of measurements, the proposed composite neural architecture allows the encoding of the complex force/deformation relationship without the need for sophisticated mathematical modeling tools. Experimental results prove the va-lidity and the feasibility of the proposed approach. Index Terms—Deformable objects, elasticity modeling, neural networks, probing guidance, range imaging, tactile sensing. I.
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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".