Planning for dynamic multiagent planar manipulation with uncertainty: a game theoretic approach
Bibliographic record
Abstract
This paper addresses the planning problem for multiagent dynamic manipulation in the plane. The objective of planning is to design the forces exerted on the object by agents with which the object can follow a given trajectory in spite of the uncertainty on pressure distribution. The main novelty of the proposed approach is the integration of noncooperative and cooperative games between agents in an hierarchical manner. Based on a dynamic model of the pushed object, the coordination problem is solved in two levels. In the lower control level, a fictitious force controller is designed by using a minimax technique to achieve the tracking performance. The design procedure is divided into two steps. First, a linear nominal controller is designed via full-state linearization with desired eigenvalues assignment. Next, a minimax control scheme is specified to optimally attenuate the worst-case effect of the uncertainty due to pressure distribution and achieve a minimax tracking performance. In the coordination level, a cooperative game is formulated between agents to distribute the fictitious force, and the objective of the game is to minimize the worst-case interaction force between agents and the object. Simulations are carried out for two-agent and three-agent manipulations, results demonstrate the effectiveness of the planning method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".