Tuning Controls for Interaction with Unstructured Environments based on Human Personality Types
Bibliographic record
Abstract
Traditional robust control system design methodologies typically provide methods for dealing with well-understood, easily-modelled disturbances of similar magnitude. However, many proposed applications for robotics require interaction with an unstructured environment, which may even include people and other robots. Providing a more advanced toolkit than simple feedback laws, fuzzy logic offers advantages in such situations; however designers often use trial-and-error (and eventually experience) in constructing their fuzzy membership sets. Moreover, many interactive tasks easily accomplished by human beings remain beyond the ability of current robotics technology. We propose a more formal methodology at the high-level design phase, based on understandings of personality types. Specifically, similarities between human personalities and robot controls allow one to choose a personality for the control as the first stage of the design process. This might provide a powerful method of tuning when one only has intuitive understanding (and order-of-magnitude estimates) of the robot, its environment, people, and other robots. Our method allows combining this type of knowledge with intuitive understanding of human personalities when tackling a control design problem. To illustrate the methodology, a design that combines four distinct personalities in a fuzzy-PID control allows a simulated system to react more reasonably, and quite differently, to unmodeled disturbances that differ by an order of magnitude.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".