Robot-assisted Rapid Prototyping for ice structures
Bibliographic record
Abstract
Ice has long been used by humankind for utilitarian purposes, and more recently for artistic and entertainment purposes. Nowadays, the field of ice construction is becoming more commercially relevant, with increased interest in ice modeling at the small scale, and in ice tourism, specifically ice hotels at the large scale. As a result, there is a market for automating ice construction, and building detailed structures that would otherwise require a significant amount of manual work. To address this demand, the authors are currently developing experimental robotic systems for building ice structures: the Fab@home, for building small-scale structures, and the Adept Cobra 600 robot, for building medium-scale structures. Further software and hardware development is needed for the Cobra, since it was not designed for rapid prototyping, and certainly not for rapid prototyping using ice as the working material. The authors have designed and built fluid delivery systems for each machine to permit the use of water as the building material. A signal-processing subsystem permits control of the water-delivery flow rate and synchronization with the robot motion. Additionally, we have developed a slicing algorithm to generate toolpaths for the Cobra using stereolithography (STL) files as the input. We also intend to develop a larger robotic system for producing ice sculptures and buildings at the architectural scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".