Advances in tactile sensors design/manufacturing and its impact on robotics applications – a review
Bibliographic record
Abstract
Abstract Purpose – Reviews the benefits and potential application of tactile sensors for use with robots. Design/methodology/approach – Includes the most recent advances in both the design/manufacturing of various tactile sensors and their applications in different industries. Although these types of sensors have been adopted in a considerable number of areas, the applications such as, medical, agricultural/livestock and food, grippers/manipulators design, prosthetic, and environmental studies have gained more popularity and are presented in this paper. Findings – Robots can perform very useful and repetitive tasks in controlled environments. However, when the robots are required to handle the unstructured and changing environments, there is a need for more elaborate means to improve their performance. In this scenario, tactile sensors can play a major role. In the unstructured environments, the robots must be able to grasp objects (or tissues, in the case of medical robots) and move objects from one location to another. Originality/value – In this work, the emphasis was on the most interesting and fast developing areas of the tactile sensors applications, including, medical, agriculture and food, grippers and manipulators design, prosthetic, and environmental studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".