Automatic Selection of Grasping Points for Shape Control of Non-Rigid Objects
Why this work is in the frame
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Bibliographic record
Abstract
The dexterous manipulation of non-rigid objects by robotic hands is a requirement for automating many delicate or labour-intensive tasks in various industries. This includes the ability to actively deform and shape objects to fit specifications, which is an important skill that allows, e.g., to insert a soft foam filter into a rigid enclosure. This work focuses on the in-hand shaping of non-rigid objects, providing an original model-free algorithm for automatically selecting the contact points between the fingers and the object's contour. This optimizes the initial conditions of the shaping task, allowing the desired shape to be approximated more efficiently with low degrees of freedom in the applied forces. The algorithm is validated experimentally with the Barrett hand and a variety of non-rigid objects.
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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 | 0.000 | 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 it