Stiffness Analysis of Underactuated Fingers and Its Application to Proprioceptive Tactile Sensing
Bibliographic record
Abstract
Underactuation has become, in recent years, more and more prevalent in robotic fingers since it provides the latter with the ability to mechanically adapt to the shape of the objects seized. To improve the usually simplistic control schemes of these fingers, and possibly to provide force feedback or control, tactile sensors are typically used. However, another promising avenue, as presented in this paper, is rather to use information provided by proprioceptive (i.e., internal) sensors. Most interestingly, this can be done using only the torque and position sensors typically found attached to the actuator(s) of these fingers. Because a relationship exists between the stiffness of an underactuated finger as seen from its actuator and the contact locations on its phalanges, it is possible to estimate one from the other. In this paper, a proprioceptive tactile sensing algorithm based on this technique is presented. It is concluded that within certain theoretical and practical limits, it is possible to extract tactile data from a self-adaptive finger, namely position and magnitude of the contact forces, without actually using any physical tactile sensors.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".