A Reinforcement Learning-Based Continuation Strategy for Autonomous On-Orbit Assembly
Bibliographic record
Abstract
Autonomous on-orbit assembly operations are critical technologies for space exploration and long-term colonization. Such operations are usually translated into complex optimal control problems requiring advanced methods to solve the resulting nonlinear programming. Nonlinear programming problems are prominent to be highly sensitive to the initial guess provided to the solver. To alleviate this situation, this paper presents a continuation strategy that significantly desensitizes the optimal control problem to the given initial guess. It became possible by forming a continuation space between a trivial problem with a known optimal solution and the desired optimal control problem using the allocation of some state, control, or constraint variables as the continuation parameters. To this end, a reinforcement learning search agent was defined based on the Sarsa(��) method to search the continuation space in real time for a continuation trajectory satisfying some desired criteria. Numerical experiments concerning the autonomous assembly of a robotic spacecraft with a complex target structure were performed to demonstrate the efficiency and capability of the proposed technique. Through various simulations, it has been shown how the complexity of the components’ shapes and the size of the problem affect the solver’s performance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".