Pretraining Using Comparable Human Activities of Daily Living Dataset in Robotic Surgical Task Learning
Bibliographic record
Abstract
Training robots to acquire surgical skills poses significant challenges, primarily due to the limited availability of comprehensive datasets and safety constraints that restrict real-time trial-and-error learning. Although human Activities of Daily Living (ADL) tasks differ substantially from surgical tasks, they encompass fundamental motor skills that can serve as a foundation for robot learning. Notably, skilled surgeons often develop their advanced surgical abilities by building upon these basic motor skills acquired through daily activities. Inspired by this progressive learning trajectory, we propose a novel surgical skill training framework that enables robots to learn basic motor skills from the ADL dataset and quickly adapt to advanced surgical skills. Specifically, we propose a unified predictive representation space, constructed using probabilistic successor features, which capture the dynamic patterns of motion primitives common to both ADL and surgical tasks. To investigate the transferability of skills from human ADL tasks to robotic surgical tasks, we conducted a mathematical analysis to evaluate transferable policies and performed simulation experiments to assess transfer performance. Furthermore, we validated the practicality and effectiveness of our method through real-world experiments. Results show that our method significantly reduces the need for extensive surgical datasets, and enables efficient learning in robotic surgical tasks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".