Communicating affect via flight path: exploring use of the laban effort system for designing affective locomotion paths
Bibliographic record
Abstract
People and animals use various kinds of motion in a multitude of ways to communicate their ideas and affective state, such as their moods or emotions. Further, people attribute affect and personalities to movements of even non-life like entities based solely on the style of their motions, e.g., the locomotion style of a geometric shape (how it moves about) can be interpreted as being shy, aggressive, etc. We investigate how robots can leverage this locomotion-style communication channel for communication with people. Specifically, our work deals with designing stylistic flying-robot locomotion paths for communicating affective state. To author and unpack the parameters of affect-oriented flying-robot locomotion styles we employ the Laban Effort System, a standard method for interpreting human motion commonly used in the performing arts. This paper describes our adaption of the Laban Effort System to author motions for flying robots, and the results of a formal experiment that investigated how various Laban Effort System parameters influence people's perception of the resulting robotic motions. We summarize with a set of guidelines for aiding designers in using the Laban Effort System to author flying robot motions to elicit desired affective responses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".