Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation
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Bibliographic record
Abstract
Modeling deformable objects is an important preliminary step for performing robotic manipulation tasks with more autonomy and dexterity. Currently, generalization capabilities in unstructured environments using analytical approaches are limited, mainly due to the lack of adaptation to changes in the object shape and properties. Therefore, this paper proposes the design and implementation of a data-driven approach, which combines machine learning techniques on graphs to estimate and predict the state and transition dynamics of deformable objects with initially undefined shape and material characteristics. The learned object model is trained using RGB-D sensor data and evaluated in terms of its ability to estimate the current state of the object shape, in addition to predicting future states with the goal to plan and support the manipulation actions of a robotic hand.
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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