Planning 3D task demonstrations of a teleoperated space robot arm
Bibliographic record
Abstract
We present an automated planning application for generating 3D tasks demonstrations involving a teleoperated robot arm on the International Space Station (ISS). A typical task demonstration involves moving the robot arm from one configuration to another. Our objective is to automatically plan the position of virtual cameras to film the arm in a manner that conveys the best awareness of the robot trajectory to the user. Given a new task, or given changes to a task previously planned, our system automatically and efficiently generates 3D demonstrations of the task without the intervention of a computer graphics programmer. For a given task, the robot trajectory is generated using a path planner. Then we consider the filming of the trajectory as a sequence of shots satisfying some temporally extended goal conveying constraints on the desirable positioning of virtual cameras. Then a temporallogic based planning system (TLPlan) is used to generate a 3D movie satisfying the goal. One motivation for this application is to eventually use it to support ground operators in planning mission tasks for the ISS. Another motivation is to eventually use automatically generated demonstrations in a 3D training simulator to provide feedback to student astronauts learning to manipulate the robot arm. Although motivated by the ISS application, the key ideas underlying our system are potentially useful for automatically filming other kinds of complex animated scenes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".