A Tactile Sensor for an Anthropomorphic Robotic Fingertip Based on Pressure Sensing and Machine Learning
Bibliographic record
Abstract
Feedback in robotic systems is imperative to accomplishing the required task. Speed controllers, visual feedback and other sensors that offer a closed-loop control system dramatically increases the effectiveness of the system. Having a sense of touch greatly increases the ability to pick-up and manipulate objects in robotic manipulation systems. Currently, precise manipulation tasks in various industries still rely on human operators, but a sense of touch on robot hands can improve the systems capabilities to perform like a human. Measuring force, contact location and detecting slip are all abilities to be desired from a tactile sensor. However, current sensors are complex to manufacture as they require different technologies to accomplish different readings. This research presents a pneumatic tactile sensor that is easy to produce and offers multiple abilities such as force feedback and slip detection using machine learning processes. The sensor showed consistent relations between its pressure readings and applied force. Slip detection was also found to be 80% confident using a decision tree classifier. The implementation of machine learning provides the improvement on other sensors to categorize data from more realistic scenarios. The results show the potential to have a one-dimensional sensor perform multiple tasks by using advanced analysis techniques.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".