Toward Active Physical Human–Robot Interaction: Quantifying the Human State During Interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unanticipated physical actions from the robot on humans [active physical human–robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a physically and psychologically safe manner. The objective of this article is to explore the possibility of quantifying the humans’ physical and mental state during an active physical interaction with a robot, by means of a laboratory experiment. We hypothesize that the active robot actions could cause measurable alterations in users’ data, which could be related to their perceptions and personalities. In the experiment, the user plays a visual game using the robot, which has a hidden task that results in active physical actions on the user. We collect data from physical and physiological sensors, and the perceptions and personalities via questionnaires and a semi-structured interview. Statistical analysis and clustering of the data collected from a total of 35 participants showed the relationships between participants’ physical and physiological data and their age, gender, perception, and personalities. Further developments based on these exploratory outcomes can be used to implement an active pHRI controller that can account for both the physical and the mental state of users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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