Hyperdimensional Gesture Recognition for Underwater Human Robot Interaction
Bibliographic record
Abstract
In this paper, we study the problem of gesture recognition as a method for divers to communicate with an underwater robot. Gesture is a common method of communication between divers, and yet autonomous underwater vehicles have very limited capacity to understand gesture given lighting and visibility constraints (e.g., from water turbidity and diver depth). Traditional deep learning methods are limited in this domain because of a lack of sufficient training data. We show that it is not enough to learn a gesture in a laboratory setting, because the appearance changes dramatically underwater. We show how hyperdimensional computing can solve this problem by permitting hypervectors to serve as abstract representations of gestures, yielding rapid adaptation to new environments and new gestures. We experimentally verify this approach using a novel dataset of 6 diving relevant gestures. We show that we can accurately adapt to a gesture learned in a laboratory setting to work with a gesture observed underwater. Our approach compares favorably to a ResNet-18, which performs well in laboratory conditions (91.9% accuracy), but performs poorly underwater (53.9% accuracy). Our proposed approach is capable of rapid adaptation, resulting in an accuracy of 83.8% on underwater gestures with just one additional example from each class added to the support set. Finally, we also show the ability to adapt to new gestures not present in our original training set. We use hypervectors to learn new gestures from the Sign Language MNIST dataset, providing a high level of accuracy with a limited amount of training data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".