Optimal Design of Self-Adaptive Fingers for Proprioceptive Tactile Sensing
Bibliographic record
Abstract
The sense of touch has always been challenging to replicate in robotics, but it can provide critical information when grasping objects. Nowadays, tactile sensing in artificial hands is usually limited to using external sensors which are typically costly, sensitive to disturbances, and impractical in certain applications. Alternative methods based on proprioceptive measurements exist to circumvent these issues but they are designed for fully actuated systems. Investigating this issue, the authors previously proposed a tactile sensing technique dedicated to underactuated, also known as self-adaptive, fingers based on measuring the stiffness of the mechanism as seen from the actuator. In this paper, a procedure to optimize the design of underactuated fingers in order to obtain the most accurate proprioceptive tactile data is presented. Since this tactile sensing algorithm is based on a one-to-one relationship between the contact location and the stiffness measured at the actuator, the accuracy of the former is optimized by maximizing the range of values of the latter, thereby minimizing the effect of an error on the stiffness estimation. The theoretical framework of the analysis is first presented, followed by the tactile sensing algorithm, and the optimization procedure itself. Finally, a novel design is proposed which includes a hidden proximal phalanx to overcome shortcomings in the sensing capabilities of the proposed method. This paper demonstrates that relatively simple modifications in the design of underactuated fingers allow to perform accurate tactile sensing without conventional external sensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".