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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".