Unified Modelling of Intelligent Robotic Systems: Applications of COH/GISMOL in Automation Engineering
Why this work is in the frame
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Bibliographic record
Abstract
Robotics and automation increasingly demand intelligent systems that integrate perception, cognition, and action while assuring safety and compliance in dynamic environments. Existing paradigms-behavior-based control, layered architectures, middleware frameworks, and learning-based controllers-solve parts of the problem but rarely offer a cohesive way to model structure, learning, and constraints together. This paper introduces Constrained Object Hierarchies (COH) and its Python toolkit GISMOL (General Intelligent System Modelling Language) as a unified approach to designing and implementing intelligent systems. COH formalizes intelligent systems via a 9-tuple representation-components, attributes, methods, neural components, embeddings, identity constraints, trigger constraints, goal constraints, and constraint daemons -thereby separating what a system is from what it does and what must never be violated. Neuroscience-inspired hierarchical processing motivates this decomposition and the separation of learned behaviors from innate constraints. We demonstrate COH/GISMOL on five representative cases in robotics and automation: warehouse AMR, collaborative assembly, autonomous harvesting, predictive maintenance, and multi-robot search-and-rescue. Across these cases, COH/GISMOL delivers: (i) systematic modelling, (ii) integrated learning with safety-first constraint enforcement, and (iii) maintainable hierarchies that support verification and runtime monitoring consistent with emerging standards and best practices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it