Model-Agnostic Meta-Learning Inspired Adaptive Control Framework for Unknown Payload Picking
Bibliographic record
Abstract
This paper presents a model-agnostic meta-learning (MAML) inspired training framework for a 7-degree-of-freedom (7-DOF) robotic manipulator, equipped with an adaptive controller to perform an object-picking task with unknown payload. Machine learning frameworks typically require large amounts of training data. While traditional meta-learning methods can adapt neural network (NN) parameters with only a few new samples, these approaches are still not fast enough for real-time robotic control tasks. To address this, a task-dependent coefficient is trained to represent the payload, and an adaptive controller is developed to adjust this coefficient in real time. A MAML inspired training algorithm is employed to produce a task-independent neural network that models all unmodeled disturbances. In this study, 10 objects of different weights are used for training, and the manipulator is tested with unknown and new payload. Simulations are conducted to demonstrate the effectiveness of the proposed training and control framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".