Iterative Learning for Gravity Compensation in Impedance Control
Why this work is in the frame
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Bibliographic record
Abstract
Robot-assisted arthroscopic surgery has been increasingly receiving attention in orthopedic surgery. To build a robot-assisted system, dynamic uncertainties can be a critical issue that could bring robot performance inaccuracy or even system instability if cannot be appropriately compensated. Disturbance observer is a common tool to be used for disturbance estimation and compensation by taking all uncertainties as disturbances, but this will refuse human–robot interaction since the human-applied force will also be regarded as a disturbance by the observer. Iterative learning for gravity compensation can be another promising way to solve this problem when gravity compensation is the main concern. In this article, a gravity iterative learning (Git) scheme in Cartesian space for gravity compensation, integrating with an impedance controller, is presented. A steady-state scaling strategy is then proposed, which released the updating requirements of the learning scheme and also extended its validity to trajectory-tracking scenarios from set-point regulations. The deriving process and convergence properties of the Git scheme are presented and theoretically analyzed, respectively. A series of simulations and physical experiments are conducted to evaluate the validity of the scaling strategy, the learning accuracy of the Git scheme, and the effectiveness of the learning-based impedance controller. Both simulation and experimental results demonstrate good performance and properties of the Git scheme and the learning-based impedance controller.
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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