An Underwater Robotic System With a Soft Continuum Manipulator for Autonomous Aquatic Grasping
Why this work is in the frame
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Bibliographic record
Abstract
Delicate underwater manipulation tasks such as biological specimen collection are promising fields that require new robotic designs and intelligent robotic technologies. In this study, we proposed an automatic aquatic object-collecting system with a soft manipulator controlled by a reinforcement learning-based controller. For underwater sensing, we implemented a visual perception framework to restore the quality of the underwater image, detect the seafood animals, and track the target's position. The online learning ability of the reinforcement learning-based controller endowed strong adaptability for the soft manipulator against underwater disturbances. The water tank grasping tests show a 91.7% successful grasping rate without flow disturbance and 83.3% with flow disturbances. We demonstrated that the soft robotic collecting system gripped seafood animals in a lab aquarium as well as the natural seabed environment. The real-world experimental results showed that the robot successfully collected 28 shells within 40 min at a water depth of 15 m and even completed grasping tasks in a dark environment. Our results demonstrated that this manipulator prototype is potentially applicable for fully autonomous delicate objects underwater.
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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 it