A Survey on Robotic Vacuum Cleaners: Evaluation of Expressive Robotic Motions based on the Framework of Laban Effort Features for Robot Personality Design
Bibliographic record
Abstract
The adoption of robotic vacuum cleaners (RVCs) has drastically increased. During interaction with these embodied autonomous agents, humans tend to ascribe certain personality traits to them even when the robot has a mechanoid appearance and low degree of freedom. As the social capabilities and the persuasiveness of robots increase, the design of robot personality will become important. This paper investigates the impact of expressive motions on people’s perception of robot personality. The framework of Laban Effort Features was implemented for a simple cleaning task. Movement features were programmed in iRobot Create2, and participants were asked to rate the robot’s personality in an online survey. The results indicated that Flow factor was closely associated with neuroticism ratings, Weight factor impacted both agreeableness and conscientiousness ratings, while Time factor impacted only the agreeableness ratings. Movement characteristics should be considered when designing personality into domestic service robots like RVCs, which are expected to operate in highly social settings.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".