Multi-agent CORBA-based robotics vision architecture for cue integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The robustness of a given vision system in the field of robotics is a very challenging problem and represents a major bottleneck in any industrial setting. Nevertheless, there is a hypothesis that the fusion of multiple natural features facilitates a robust detection and object tracking in scenes of real world complexity. Several fusion methods have been tested for cue integration with good results, but the computational effort grows as the number of features increases. This research work represents a variant of the fusion method based both on distributed systems and on an agent concept. In this work, multiple agents interact with each other to perform different roles. The structure has a cooperative approach so that the agents work as a team. The communication among the agents is based on the Event Service of CORBA technology. By using this architecture, we are exploiting the parallelism and concurrency of distributed systems, and by using the concept of agents we are exploiting the encapsulation concept to built modular systems.
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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 | 0.000 | 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 it