Towards Safe and Efficient Reinforcement Learning for Surgical Robots Using Real-Time Human Supervision and Demonstration
Bibliographic record
Abstract
Recent research in surgical robotics has focused on increasing the level of autonomy in order to reduce the workload of surgeons. While deep reinforcement learning (DRL) has shown promising results in automating some surgical subtasks, due to its demand for a large number of random explorations, safety and learning efficiency remain the primary challenges when applying it to surgical robot learning. In this work, we present a DRL framework with real-time human supervision during the training process for surgical robot learning to avoid significant failures and speed up training. A novel training methodology based on the combination of DRL and generative adversarial imitation learning (GAIL) is proposed to further improve learning efficiency by imitating human behaviors. The proposed method is validated using two simulated environments, where human intervention is performed via teleoperation. Results show that our method outperforms baseline algorithms and can achieve safe and efficient learning.
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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 | 0.000 | 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 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".