A socially assistive robot that can interpret affective body language during one-on-one human-robot interactions
Bibliographic record
Abstract
Socially assistive robots can engage in assistive human-robot interactions (HRI) by providing rehabilitation of cognitive, social, and physical abilities after a stroke, accident or diagnosis of a social, developmental or cognitive disorder. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: a robot’s ability to identify human non-verbal communication during assistive interactions. Namely, we present a unique non-contact automated sensory-based approach for identification and categorisation of human upper body language in determining how accessible a person is to a robot during natural real-time HRI. This classification will allow a robot to effectively determine its own reactive task-driven behaviour during assistive interactions. The types of interactions envisioned include providing reminders, health monitoring, and social and cognitive therapies. Preliminary experiments presented show the potential of integrating the proposed body language recognition and classification technique into a socially assistive robot partaking participating in HRI scenarios.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".