Discerning Affect From Touch and Gaze During Interaction With a Robot Pet
Why this work is in the frame
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Bibliographic record
Abstract
Practical affect recognition needs to be efficient and unobtrusive in interactive contexts. One approach to a robust realtime system is to sense and automatically integrate multiple nonverbal sources. We investigated how users’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">touch</i> , and secondarily <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gaze</i> , perform as affect-encoding modalities during physical interaction with a robot pet, in comparison to more-studied biometric channels. To elicit authentically experienced emotions, participants recounted two intense memories of opposing polarity in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stressed</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Relaxed</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Depressed</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Excited</i> conditions. We collected data (N=30) from a touch sensor embedded under robot fur (force magnitude and location), a robot-adjacent gaze tracker (location), and biometric sensors (skin conductance, blood volume pulse, respiration rate). Cross-validation of Random Forest classifiers achieved best-case accuracy for combined touch-with-gaze approaching that of biometric results: where training and test sets include adjacent temporal windows, subject-dependent prediction was 94% accurate. In contrast, subject-independent Leave-One-participant-Out predictions resulted in 30% accuracy (chance 25%). Performance was best where participant information was available in both training and test sets. Addressing computational robustness for dynamic, adaptive realtime interactions, we analyzed subsets of our multimodal feature set, varying sample rates and window sizes. We summarize design directions based on these parameters for this touch-based, affective, and hard, realtime robot interaction application.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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