Soft robotics: Definition and research issues
Bibliographic record
Abstract
Soft robots have been a buzz word in the field of robotics. However, there are several definitions of soft robots in literature with their varying degrees of usefulness to providing guidelines for designing and constructing a soft robot. This paper attempts to provide a comprehensive definition of soft robots with the goals that the definition should be of (i) generality and (ii) practicality. By generality, it is meant that the definition gives the signature to soft robots and is all inclusive, and by practicality, it is meant that the definition provides a general guideline for practicing designers to design and construct soft robots to particular applications. A salient point in our definition of soft robots is the concept of softness, which is defined from the perspective of a receiving object - particularly as the stress and other damage quantities (e.g., deflection) created in the receiving object when the receiving object interacts with the soft system per se. A further contribution is the provision of definitions to soft sensor, soft actuator along with power generator, soft controller, and soft mechanism. Finally, research issues on soft robots are outlined.
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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".