Iterative Learning for Gravity Compensation in Impedance Control
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".