A survey on the design and evolution of social robots — Past, present and future
Bibliographic record
Abstract
Despite the relatively young age of Human–Robot Interaction (HRI) as a field, there is a large volume of research on advances in robot hardware, software and behavior. The goal of this article is to survey trends in social robot design, to provide an evidence-based approach and guidelines that can inform future social robot development. To this end, this article systematically reviews the evolution of social robots with a focus on their applications, technical features and design. In total 9920 articles from ACM Digital Library (n=4223) and IEEE Explore (n=5697) were reviewed. In order to make this review as inclusive as possible, a broad definition of social robots was used to make decisions about inclusion/exclusion of a given social robot during the review process. As a result, a total of 344 social robots were examined in the review with features being embodiment, mobility, total number of degrees of freedom, existence of a manipulator, size, weight, shell build, applications, target user group, commercial availability, social software capabilities, sensors, interaction modalities, face, software extension capability and initial release year. This resulted in a rich dataset with detailed information about the social robots used in the HRI field. We also provide design guidelines for social robots to inform future research. Findings of this review may help both researchers & practitioners to select, and/or design, the best social robot for their particular experiment or application scenario.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".