Evaluation of Expressive Motions based on the Framework of Laban Effort Features for Social Attributes of Robots
Bibliographic record
Abstract
In today’s world, it is not uncommon to see robots adopted in various domains and environments. Robots take over several roles and tasks from manufacturing facilities to households and offices. It is crucial to measure people’s judgment of robots' social attributes since the findings can shape the future design for social robots. Using only a simple and mono-functional robotic vacuum cleaner, this paper investigates the impact of expressive motions on how people perceive the social attributes of the robot. The Laban Effort Features, a framework for movement analysis that emerged from dance, was modified to design expressive motions for a simple cleaning task. Participants were asked to rate the social attributes of the robot under several treatment conditions using a video-based online survey. The results indicated that velocity influenced people’s ratings of the robot’s warmth and competence, while path planning behavior influenced people’s ratings of the robot’s competence and discomfort. Limitations of this study include the kinematic constraints of the robot, potential issues with survey design, and technical constraints related to the open interface provided by the robot’s developer. The findings should be considered when incorporating expressive motions into domestic service robots operating in social settings.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".