Vision-based Autonomous Blood Suction with a Concentric Tube Continuum Robot
Bibliographic record
Abstract
Blood-water mixture removal is essential in many surgeries. Existing works adding a robotic assistant primarily focus on conventional robots. Limited attention has been given to using continuum robots for this task. This paper introduces a vision-based control framework for autonomous liquid suction using a concentric tube continuum robot (CTCR). The proposed method employs a controller using camera input combined with a hybrid control strategy that integrates differential inverse kinematics and a pre-computed lookup table to ensure stable and precise motion during suction. A CTCR simulator, implemented in the Unity Game Engine with photorealistic rendering and robot-liquid interaction capabilities, as well as a benchtop robot system were developed as the experiment platform. The proposed method was evaluated through simulation and real-world experiments across four scenarios, demonstrating its generalizability and stability. In 32 real-world trials, less than 0.1 g of liquid remained after the suction, while over 99% of liquid was removed across 32 simulated trials. The results highlight the potential of CTCR for autonomous surgical liquid suction, showcasing the system’s adaptability and performance in dynamic environments.
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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".