Situated robot design with prioritized constraints
Bibliographic record
Abstract
The constraint-based agent (CBA) framework with prioritized constraints is a simple and effective methodology for designing and building situated robots. This methodology can be seen as a formal development of the subsumption approach. It prevents ad hoc layering of behaviors and supports modular development of the system. A situated robot called Ainia that repeatedly finds, tracks, chases, and kicks a ball is presented as an illustrative case study of this design methodology. Ainia is first modeled, simulated, and animated with the constraint nets in Java (CNJ) tool. Then, a prioritized constraint-based controller of the simulated Ainia is used to control the physical robot in the real world. The results show that the behaviors of the physical robot satisfy the requirements specification. Hence, this study provides evidence that the formal CBA framework with prioritized constraints is an effective approach for synthesizing situated robot controllers. In addition, it supports the claim that CNJ is an effective tool for designing and building situated robots operating in the real world.
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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 | 0.001 | 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".