High-Fidelity General-Purpose Robotic Simulation Framework for Artificially Intelligent Space Exploration Vehicles
Bibliographic record
Abstract
This paper presents the design, open-problems, and preliminary results of a long-term research project into the development of a sophisticated simulation framework for artificially intelligent space exploration vehicles. This research project uses popular open-source software libraries and tools from the academic literature as its base and extends on top of it. The purpose of this project is to improve the current state of general-purpose robotic simulation technology in order to improve real-world space exploration vehicles and the artificially intelligent algorithms that they deploy. Artificially intelligent space exploration vehicles are dependent on simulation technology because simulators are used throughout the entire design and development process, and this means that the state, accuracy, and capabilities of the simulation technology is very indicative to the future of space exploration. General-purpose robotic simulators are a special class of simulator designed to simulate everything inherent in a real-world robotics application. The problem with the current general-purpose robotic simulation technology is that the behavior of the simulated vehicles is not realistic enough and does not emulate the real-world to a high enough degree of accuracy. The goal of the research being presented in this paper is to greatly increase the precision and accuracy of the vehicles behavior so as to mimic the real-world behavior as closely as possible, which in turn will produce better real-world artificially intelligent algorithms and space vehicles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".