A survey on the design and evolution of social robots — Past, present and future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it