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
The use of games as vehicles to study human-robot interaction (HRI) has been established as a suitable solution to create more realistic and naturalistic opportunities to investigate human behavior. In particular, multiplayer games that involve at least two human players and one or more robots have raised the attention of the research community. This article proposes a scoping review to qualitatively examine the literature on the use of multiplayer games in HRI scenarios employing embodied robots aiming to find experimental patterns and common game design elements. We find that researchers have been using multiplayer games in a wide variety of applications in HRI, including training, entertainment and education, allowing robots to take different roles. Moreover, robots have included different capabilities and sensing technologies, and elements such as external screens or motion controllers were used to foster gameplay. Based on our findings, we propose a design taxonomy called Robo Ludens, which identifies HRI elements and game design fundamentals and classifies important components used in multiplayer HRI scenarios. The Robo Ludens taxonomy covers considerations from a robot-oriented perspective as well as game design aspects to provide a comprehensive list of elements that can foster gameplay and bring enjoyable experiences in HRI scenarios.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.007 |
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