Modeling and analysis of dynamic multi-agent planar manipulation
Bibliographic record
Abstract
A dynamic model for multi-agent manipulation is investigated under a nonlinear control framework. The motion of the rigid body on a plane under two-finger pushing is modeled as a nonlinear system. The local controllability is derived for different cooperation patterns. The motion of the rigid body under pushing is composed of two stages: during the first stage, the fingers maintain contact with the object, and through adjusting the pushing position, orientation and force on the object, the configuration of the object can reach some subsets in configuration space. Due to local contact constraints, the fingers may lose contact with the object where the second stage of motion starts. Here, the motion of the object is governed by the friction force only. Thus manipulation planning consists of two parts. First, an initial state response problem needs to be solved to find the subset of configuration space which can reach the final configuration under the friction-governed sliding. The remaining problem can be categorized as optimal control problem, which solves the cooperation between agents which force the object moving into the subset found by the initial state response problem. The possibility of cooperation is illustrated through an example.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".