Robot Learning Incorporating Human Interventions in the Real World for Autonomous Surgical Endoscopic Camera Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent studies in surgical robotics have focused on automating common surgical subtasks such as grasping and manipulation using deep reinforcement learning (DRL). In this work, we consider surgical endoscopic camera control for object tracking e.g. using the endoscopic camera manipulator (ECM) from the da Vinci Research Kit (dVRK) (Intuitive Inc., Sunnyvale, CA, USA) as a typical surgical robot learning task. A DRL policy for controlling the robot joint space movements is first trained in a simulation environment and then continues the learning in the real world. To speed up training and avoid significant failures (in this case, losing view of the object), human interventions are incorporated into the training process and regular DRL is combined with generative adversarial imitation learning (GAIL) to encourage imitating human behaviors. Experiments show that an average reward of 159.8 can be achieved within 1000 steps compared to only 121.8 without human interventions, and the view of the moving object is lost only twice during the training process out of 3 trials. These results show that human interventions can improve learning speed and significantly reduce failures during the training process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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