Intuitiveness Level: Frustration-Based Methodology for Human–Robot Interaction Gesture Elicitation
Bibliographic record
Abstract
For robotics to become more accessible to people not specialized in the area, it is of fundamental importance to improve and simplify the way people interact with robots. Despite human-robot interaction (HRI) being an effervescent research area, most of the works published so far on the use of gesture interfaces for human-robot communication do not clearly describe how the used gestures were elicited, thus hindering the reproducibility of those works. Considering this, we propose a new and reproducible Frustration-Based Approach (FBA), scientifically established on previous research, which can be used to obtain an intuitive and robust gesture vocabulary for HRI. To accomplish this, we propose Intuitiveness Level (IL), a score to rank gestures according with its intuitiveness. Using IL, it is possible to conceive a complex vocabulary, allowing an increasing of robustness, since more than one gesture can be associated to a task. In a general sense, the proposed methodology is not limited only for HRI, and it can also be used for human-machine interaction in general. In short, the contributions of this work are: (i) A complete methodology to elicit gestures to be used as intuitive communication interface between humans and robots. (ii)A metric of intuitiveness which takes into account at least three different characteristics about the elicited gestures.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".