Learning Graph Dynamics With Interaction Effects Propagation for Deformable Linear Objects Shape Control

arXiv 2025 35.7 method, application

TLDR

Proposes GA-Net, a GNN with attention for learning DLO deformation dynamics and MPC-based shape control, validated in simulation and real-world experiments.

Reasoning

The paper presents a focused approach for deformable linear object dynamics learning and control, with strong empirical validation including real-world experiments. Its scope is narrow, and the connection to general world models or reinforcement learning is limited.

Read-first score

Read-first score 35.7, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 15.

Citation impact 15%

94.2

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. citation_normalized_percentile=0.94242643

Recency 5%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=baseline,experiment

Citation velocity 10%

38.4

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=5.50

Topical relevance 25%

21.4

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=False; code=False; dataset=False; markers=github

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Field roles

FoundationFrontier

Rank sensitivity

Stability: volatile; rank range: 605.

Keyword Scores

world dynamics prediction

world model

model-based reinforcement learning world model

world simulator

interactive world model

generative world model

video world model