3 citations · 3 across the 6 of their papers we have counts for
10 papers
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs
Yingdong Ru, Lipeng Zhuang, Zhuo He +1
We present ContinuousCloth, a continuous-time, physics-informed neural solver for deformable cloth dynamics that enforces a spring--mass ODE within a neural trajectory model. Unlik…
ClothCode: Generative Modeling for Garment State Estimation and Dynamics Prediction
Yingdong Ru, Lipeng Zhuang, Shiyu Fan +3
We introduce ClothCode, a unified generative framework for garment state estimation and action-conditioned dynamics prediction in a shared discrete configuration space. A material-…
Masked Generative Motion Planning with Geometry-Guided Token Search
Lipeng Zhuang, Yingdong Ru, Shiyu Fan +3
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement. We introduce Masked Genera…
FoldBack: Self-Correcting Masked Generative Policy for Long-Horizon Garment Folding
Lipeng Zhuang, Shiyu Fan, Yingdong Ru +4
We present FoldBack, a self-correcting masked generative policy for long-horizon garment folding. Existing long-trajectory policies may continue after a missed or slipped grasp eve…
Masked Generative Policy for Robotic Control
Lipeng Zhuang, Shiyu Fan, Florent P. Audonnet +4
We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer…
Can Real-to-Sim Approaches Capture Dynamic Fabric Behavior for Robotic Fabric Manipulation?
Yingdong Ru, Lipeng Zhuang, Zhuo He +2
This paper presents a rigorous evaluation of Real-to-Sim parameter estimation approaches for fabric manipulation in robotics. The study systematically assesses three state-of-the-a…
Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation
Lipeng Zhuang, Shiyu Fan, Yingdong Ru +3
We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations…
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning
Zijun Long, Lipeng Zhuang, George Killick +3
Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-rese…
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion
Zijun Long, George Killick, Lipeng Zhuang +3
State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning…
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning
Zijun Long, George Killick, Lipeng Zhuang +3
Image classification datasets exhibit a non-negligible fraction of mislabeled examples, often due to human error when one class superficially resembles another. This issue poses ch…