Efficient Sampling for Rapid Estimation of 3-D Stiffness Distribution via Active Tactile Exploration
Bibliographic record
Abstract
This article proposes a novel efficient sampling strategy to rapidly estimate the distribution of stiffness over an inhomogeneous object with a highly limited number of sample points taken from the object surface. The stiffness on the object surface is modeled as a mass-spring system, and its distribution is estimated via tactile exploration using Gaussian process regression. The main objective of this article is to improve the efficiency of the estimation process while producing an accurate estimate for both the overall distribution and some particular areas (i.e., high/low stiff areas). Specifically, the mutual information is employed to quantify the amount of information on the whole space of interest provided by each sample point. The estimated stiffness distribution is also taken into account to locate the extreme stiffness areas. An objective function that consists of these two criteria is proposed to optimally balance between the exploration of the unobserved regions and exploitation of certain local areas that have high/low stiffness. Physical experiments on a variety of inhomogeneous objects demonstrate the advantage of the proposed algorithm in comparison to a popular existing algorithm in terms of accuracy and estimation speed.
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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".