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
A new generation of humanoid robots is emerging to work together with, or even replace, human operators performing complex dextrous manipulation operations in a variety of applications such as health and elder care, hazardous or high-risk environments, telemedicine, or manufacturing. To meet the challenging operational requirements of such applications, this new generation of humanoid robots should not only look as humans, but should also behave like them, being able to sense and perceive the external world and perform tasks as humans do. Touch sensing and perception is essential when handling objects while working on such complex activities in unstructured environments. The major challenges encountered when replicating the human touch sensing mechanisms are due to the inherently low resolution of the tactile images produced by the artificial sensors, to the complexity of interpreting the sensor data, and to the fact that robot hand technology is still clumsy when compared with the nimble dexterity of the human hand and fingers. This paper presents practical touch sensing solutions for humanoid robots (Fig. 1) that mimic the complex sensing mechanisms occurring in a human hand while exploring by touch 3D objects.
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