Tactile sensing based on fingertip suction flow for submerged dexterous manipulation
Bibliographic record
Abstract
The ocean is a harsh and unstructured environment for robotic systems; high ambient pressures, saltwater corrosion and low-light conditions demand machines with robust electrical and mechanical parts that are able to sense and respond to the environment. Prior work shows that the addition of gentle suction flow to the hands of underwater robots can aid in the handling of objects during mobile manipulation tasks. The current paper explores using this suction flow mechanism as a new modality for tactile sensing; by monitoring orifice occlusion we can get a sense of how objects make contact in the hand. The electronics required for this sensor can be located remotely from the hand and the signal is insensitive to large changes in ambient pressure associated with diving depth. In this study, suction is applied to the fingertips of a two-fingered compliant gripper and suction-based tactile sensing is monitored while an object is pulled out of a pinch grasp. As a proof of concept, a recurrent neural network model was trained to predict external force trends using only the suction signals. This tactile sensing modality holds the potential to enable automated robotic behaviors or to provide operators of remotely operated vehicles with additional feedback in a robust fashion suitable for ocean deployment.
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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 | 0.000 | 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) | 0.001 | 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 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".