Special issue on machine learning and data engineering in robotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the growth of sensor data and networked devices, machine learning has been gaining interest both in robotics and many other disciplines. The collaboration between machine learning and data engineering in robotics could have a strong impact on the way robots or services help our lives as well as on deepening our understanding of human intelligence. This special issue attempts to showcase state-of-the-art studies ranging from sensor data management through learning real-world data or behaviors as well as probabilistic models in constructive approaches. Emphasis will be given to novel algorithms and theories in the field, quantitatively comparable research results, robotic applications that help people, and constructive approaches that model human cognitive skills. We solicit original and high-quality papers on machine learning and data engineering in robotics. We also solicit survey papers that discuss open challenges and state-of-the-art techniques. Topics of interest include, but are not limited to:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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