Centralized cooperative planning for dynamic multi-agent planar manipulation
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Bibliographic record
Abstract
This paper studies the coordination problem for dynamic multi-agent planar manipulation, and proposed a novel centralized cooperative planning method based on backstepping design and quadratic programming. The objective of coordination is to plan interaction forces between agents and object, such that the object can follow a given trajectory, and the forces satisfy the predefined performance index. In this paper, the coordination problem is solved hierarchically in two levels. In the lower control level, a generalized force input is designed using the backstepping technique, with which the agents can control the object tracking a given trajectory. In the higher coordination level, the distribution of forces between agents is discussed in a quadratic programming framework, and the optimal force distribution is found by solving a quadratic programming problem. Simulations are carried out for two-agent and three-agent manipulations, and the results obtained demonstrate the effectiveness of the proposed coordination method.
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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 | 0.000 | 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 it