Touch sensing for humanoid robots
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.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".