Practical considerations on proprioceptive tactile sensing for underactuated fingers
Bibliographic record
Abstract
Underactuated mechanisms are becoming more prevalent in new robotic graspers, partly because of the desire to reduce the complexity and associated costs of conventional fully actuated systems. With the same objective of reducing the costs of the components needed to provide a sensory feedback, several authors have worked on finding alternatives to external tactile sensors. This paper is about one of these methods, namely proprioceptive tactile sensing, especially designed for underactuated fingers. It focuses on certain practical considerations, such as the impact of the curvature of the grasped object and the reconfiguration of the finger after the contact, and proposes the analysis of their influence on the precision of the algorithm. To this aim, simulations and experimental data are provided for different grasping scenarios. It is shown that the effect of local curvature remains limited compared with other causes of imprecision such as friction in the system. It is also demonstrated that the reconfiguration, if within reasonable limits, does not cause significant variations on the estimation of the contact location.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".