Self-Optimization of Robot Design for Navigating in Ceiling Systems
Bibliographic record
Abstract
Suspended ceiling systems are complex and heterogeneous due to the combination of different components. Therefore, it is hard for robots to navigate in these environments without expert designed robot morphology. However, the cost of robot design is high since ceiling systems vary from building to building. Currently, some studies demonstrate that robots can evolve like creatures so that they can adapt themselves to different environments through evolutionary strategies. Inspired by the assembly of LEGO bricks, we applied graph grammar methods to optimize robot design in suspended ceiling systems. The basic idea is that the robot can find the optimized structures assembled from elementary components for themselves without any human intervention. Robots were trained in four common ceiling environments reflecting the influence of typical terrain and obstructions (e.g., ducts). Results show that different suspended ceiling system significantly affects the configuration of robots and robots can successfully evolve specific shapes to improve their acclimatization. This paper marks the first attempt at performing robot evolution in the context of facilities management and is expected to evoke future discussions in robot design in more civil engineering tasks.
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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 | 0.001 | 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".