A Vision-Based Control and Interaction Framework for a Legged Underwater Robot
Why this work is in the frame
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Bibliographic record
Abstract
We present a vision-based control and interaction framework for mobile robots, and describe its implementation in a legged amphibious robot. The control scheme enables the robot to navigate, follow targets of interest, and interact with human operators. The visual framework presented in this paper enables deployment of the vehicle in underwater environments along with a human scuba diver as the operator, without requiring any external tethered control. We present the current implementation of this framework in our particular family of underwater robots, with a focus on the underlying software and hardware infrastructure. We look at the practical issues pertaining to system implementation as it applies to our framework, from choice of operating systems to communication bus design. While our system has been effectively used in both open-ocean andclosed-water environments, we perform some quantitative measurements with an effort to analyze the responsiveness and robustness of the complete architecture.
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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