Reinforcement-Learning-Based Continuation Strategy for Autonomous On-Orbit Assembly
Bibliographic record
Abstract
Autonomous on-orbit assembly operations are usually translated into complex optimal control problems requiring advanced methods to solve. Solved through either direct methods or indirect methods, these optimal control problems are known to be highly sensitive to the initial guess provided to the solver. This paper proposes an algorithm to reduce the problem’s sensitivity to the initial guess by integrating the continuation method and the notion of reinforcement learning. To this end, a continuation space is formed from a trivial problem with a known solution to the desired optimal control problem. A set of continuation parameters, selected from the state, control, or constraint variables, forms the basis for this continuation space. Finding a suitable continuation path is problematic since it requires a comprehensive search algorithm that considers several factors, such as the choice of continuation parameters and continuation steps for each parameter, to find an appropriate path. To search for this path, a reinforcement learning search agent was proposed based on the Sarsa([Formula: see text]) method to traverse the continuation space in real-time for a continuation trajectory satisfying some desired criteria. Numerical experiments were conducted for a series of autonomous on-orbit assembly operations using robotic spacecraft to demonstrate the capability of the proposed technique. Various simulations with different reward functions showed that the shape and size of the objects in the assembly environment affect the solver’s performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".