Visual monitoring of surface deformations on objects manipulated with a robotic hand
Bibliographic record
Abstract
Nowadays dexterous manipulation of rigid objects using a robot hand can be achieved fairly well. However, grasping and manipulating deformable objects is still challenging as the force and tactile sensors which are commonly used in such applications can only provide local information about the deformation at the contact points. In this paper, a vision framework is proposed for 3D visually guided grasping and manipulation of deformable objects. This visual monitoring framework, which uses state-of-the-art computer vision methods, provides a robotic hand system with comprehensive monitoring of the deformable object that it manipulates as it tracks its deformation. Stereoscopic vision is used to detect and track in real time the deformation of non-rigid objects in three dimensions and within a complex environment. The technique is tested successfully in real robotic operation conditions using the Barrett hand. The actual object shape is rendered in the 3D virtual environment of the GraspIt! robotic simulator which also displays the hand configuration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".