Master–Slave Safe Cooperative Tracking via Game and Learning-Based Shared Control
Why this work is in the frame
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Bibliographic record
Abstract
This work studies the two-player cooperative tracking problem with the players interacting in a master–slave scheme.This problem is formulated as an asymmetric differential game, where the control strategy of the master is unmodifiable and its optimization criterion needs to be recovered. To solve this problem, we develop a learning-based algorithm involving inverse optimization and forward optimal control to estimate the master cost parameter and design the slave shared controller simultaneously. A sharing rule based on the estimation of the master cost parameter is proposed, which makes the interaction effective by affecting the control effort paid by the slave directly and continuously. In addition, by using a Lyapunov-like control barrier function, we design a novel safety-critical controller, which can be combined with the shared controller to realize safe trajectory tracking. Some simulation results are given to illustrate the effectiveness of the proposed approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it