Advanced space robotics simulation for training and operations
Bibliographic record
Abstract
This paper describes the advances being made in space robotics simulation to meet the challenges of astronaut training and space operations support. This simulator (MOTS) is being used to train International Space Station astronauts to perform on-orbit robotics tasks. It also supports mission planning and task verification in an operationally representative environment. The simulator supports critical tasks to be performed by astronauts including payload handling, berthing and de-berthing. MOTS is a state-of-the-art simulator providing astronauts with a simulation representative of the space station dynamics and visual environment. It provides real-time high-fidelity simulation of the flexible dynamics performance of two robotic arms (space station arm and shuttle arm) concurrently in a micro-gravity environment to support complex hand-off tasks. Contact dynamics models have been added to enhance the realism of berthing payloads to the Space Station with multiple contact points simultaneously tracked. 3D visual models support realistic views generated by the space station cameras in an operational and dynamic lighting environment that includes the production of split screen views. The incorporation of the Space Station Robot Arm Flight Control System Software provides an invaluable and confident environment in which on-orbit tasks is being planned and practiced. MOTS is also being integrated into several facilities at the Canadian Space Agency such as the Space Operations and Support Centre, to support on-line diagnostics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".