Communication with Self-Growing Character to Develop Physically Growing Robot Toy Agent
Bibliographic record
Abstract
Robots for communication are developed extensively with an emphasis on sympathy. This study deals with the growth of character and the control of its operation. The child has time to be alone with the nature of his/her robot friend. That child can interact with other people’s emotional expressions through a robot. Step by step, the robot character will grow as the child grows. Through design studies, qualitative processes such as customer experience audit, eye tracking, mental model diagrams, and semantic differences have been executed for the results. The participatory behavior research approach through user travel is mapped from the user’s lead to the evidence-based design. This research considers how synthetic characteristics can be applied to the physical growth of robot toys through the product design process. With the development of robot toy “Buddy”, two variations on the robot were made to achieve recognizable growth. (1) one-dimensional height scaling and (2) facial expression including the distance between two eyes on the screen. Observations represented children’s reactions when "Buddy" was released with the children. As an independent synthetic character, the robot was recognized by children who had the designed function. Robots for training may require more experimentation.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".