Multi-agent cooperative manipulation with uncertainty: a neural net-based game theoretic approach
Bibliographic record
Abstract
This paper proposes a novel planning method for multi-agent dynamic manipulation on a plane. The objective of planning is to find optimal forces exerted on the object by agents with which the object can follow a given trajectory. The main contributions of the proposed approach is: First, through integrating of noncooperative game and neural-net approximation, the planner can deal with unknown pressure distribution effectively. Second, by introducing cooperative game between agents, the forces exerted by agents distributed optimally. Based on the dynamic model of the pushed object, the planing problem is solved in two levels hierarchically. In the lower control level, generalized force inputs are designed by using minimax technique to achieve the tracking performance. In the coordination level, cooperative game is formulated between agents to distribute the generalized force, and the objective of the game is to minimize the worst case interaction force between agents and object. Simulations are carried out for the three-agent cooperative manipulation, results demonstrate the effectiveness of the proposed 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".