Bots of a Feather: Exploring User Perceptions of Group Cohesiveness for Application in Robotic Swarms
Bibliographic record
Abstract
Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".