Model-Free Magnetic Servoing Control: Leveraging Raw Magnetic Data for Robotic Manipulation
Bibliographic record
Abstract
This article introduces a novel, model-free magnetic servoing control approach for robotic manipulation, typically employed for specified 6-DoF pose following or trajectory tracking, using raw magnetic data acquired from a magnetometer array. Conventional closed-loop magnetic servoing control of robot manipulators requires an accurate model that correlates robot motion with real-time magnetic field measurements. However, this modeling is complex due to the high degree of nonlinearity in magnetic field calculations. Moreover, measurement errors or model inaccuracies can adversely affect control outcomes. In this study, we attach two orthogonal magnets to the robot end-effector to facilitate its 6-DoF control. To enhance control stability and convergence rate, we make a Jacobian consistency assumption and implement closed-loop control that incorporates a moving window of historical error and actuation data. Furthermore, an adaptive extended Kalman filter is utilized to dynamically estimate the Jacobian matrix and update the noise covariance matrices. As a result, the magnetic servoing control can be executed without any prior knowledge of the magnetic model. Experiments are finally conducted by tracking specified 6-DoF poses and different trajectories with the robot end-effector. The results validate the stability and efficiency of our proposed method.
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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 | 0.000 | 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 | 0.001 | 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 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".