(e)motion: Exploring the Affect of Abstract Motion in Human-Robot Interaction
Bibliographic record
Abstract
In this paper we present our exploration of the emotional impact robot motion has on humans.We argue and attempt to justify the exploration of a fundamental layer of physical motion, trying to understand how it is being interpreted by observers.We discuss our design philosophy, attempting to create an abstract robotic platform, formless and affordances-less, and to examine it in an exploratory fashion; allowing participants to reflect on the motion they experience in various open ended ways.We argue that through our observations we could be able to achieve insight into how different robotic motions map to emotion, insight that could have implications for design well beyond abstract robotic interfaces.The paper discusses our early prototype efforts and their design critique evaluation.It then follows by presenting our final prototype and an extensive user study we performed using it; attempting to understand whether and how basic robot movements, conveyed via an abstract robotic platform, can elicit emotional reactions and engagement in users.We detail and discuss our findings and their significance to the domain of social human-robot interaction design.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".