The Good Grasp, the Bad Grasp, and the Plateau in Tactile-Based Grasp Stability Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Research around tactile sensing for grasp stability prediction in robotic manipulators continues to be popular, however few works are able to achieve a high classification accuracy. Due to simulation complexity, data-driven methods are often forced to rely on experimental data, yielding small, often unbalanced, data sets. In this work, the authors use a 3972 sample data set to explore the effects of the data set composition on the performance of a classifier. While maintaining a similar overall accuracy, the ability to recognize a grasp failure was significantly impacted by the composition of the data set. The authors propose an autonomous pipeline designed to generate more diverse failure grasps. On failure-rich data, a tactile-based classifier with a balanced training set achieved a classification accuracy of 84.68% while maintaining a recall of the grasp failure class of 76%. This represents a 71.79% improvement in recall over a model trained on a larger but unbalanced data set.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it