Social Intelligence for a Task-Driven Assistive Robot
Bibliographic record
Abstract
The objective of a socially assistive robot is to create a close and effective interaction with a human user for the purpose of giving assistance. In particular, the social interaction, guidance and support that a socially assistive robot can provide a person can be very beneficial to patient-centered care. However, there are a number of conundrums that must be addressed in designing such a robot. This work addresses one of the main limitations in the development of intelligent task-driven socially assistive robots: Robotic control architecture design and implementation with explicit social and assistive task functionalities. In particular, in this paper, a unique emotional behavior module is presented and implemented in a learning-based control architecture for human-robot interactions (HRI). The module is utilized to determine the appropriate emotions of the robot, as motivated by the well-being of the person, during assistive task-driven interactions. A novel online updating technique is used in order to allow the emotional model to adapt to new people and scenarios. Preliminary experiments presented show the effectiveness of utilizing robotic emotional assistive behavior during HRI in assistive scenarios.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".