Learn2Fold generates physically valid origami folding sequences from text prompts by decoupling LLM-based program proposals from verification in a learned graph-structured world model.
Learning a general model: Folding clothing with topologi- cal dynamics
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A GNN-based network reconstructs garment seams into a skeleton graph for state estimation, enabling a hierarchical visual servoing controller that achieves human-level alignment accuracy and robustness across garments on a bimanual robot.
citing papers explorer
-
Learn2Fold: Structured Origami Generation with World Model Planning
Learn2Fold generates physically valid origami folding sequences from text prompts by decoupling LLM-based program proposals from verification in a learned graph-structured world model.
-
Seam-to-Graph Reconstruction for Garment Configuration Alignment
A GNN-based network reconstructs garment seams into a skeleton graph for state estimation, enabling a hierarchical visual servoing controller that achieves human-level alignment accuracy and robustness across garments on a bimanual robot.