Label-Free Adaptive Gaussian Sample Consensus Framework for Learning From Perfect and Imperfect Demonstrations
Bibliographic record
Abstract
Autonomous robotic surgery represents one of the most groundbreaking advancements in medical technology. Learning from human demonstrations is promising in this domain, which facilitates the transfer of skills from humans to robots. However, the practical application of this method is challenged by the difficulty of acquiring high-quality demonstrations. Surgical tasks often involve complex manipulations and stringent precision requirements, leading to frequent errors in the demonstrations. These imperfect demonstrations adversely affect the performance of controller policies learned from the data. Unlike existing methods that rely on extensive human labeling of demonstrated trajectories, we present a novel label-free adaptive Gaussian sample consensus framework to progressively refine the control policy. We demonstrate the efficacy and practicality of our approach through two experimental studies: a handwriting classification task, providing reproducible ground-truth labels for evaluation, and an endoscopy scanning task, demonstrating the feasibility of our method in a real-world clinical context. Both experiments highlight our method’s capacity to efficiently adapt to and learn from an ongoing stream of imperfect demonstrations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".