Optimal robot selection by gaze direction in multi-human multi-robot interaction
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a computer vision based system for interaction between multiple humans and multiple robots. Each human can “select” (obtain the undivided attention of) a robot by simply looking directly at it. This extends previous work whereby a single human can select one or more robots from a population. Each robot optimally assigns human identities to tracked faces in its camera view using a local Hungarian algorithm. The gaze-direction and location of the faces are estimated via vision, and a score for each robot-face pair is assigned. Then the system finds the global optimal allocation of robot-to-human selections using a centralized Hungarian algorithm. A useful feature of this method is that robots can be selected by people they cannot see. This is the first demonstration of optimal many-to-many robot-selection HRI.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it