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Found 6 papers, 3 papers with code
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
In particular, we leverage a latent representation of elastic physical properties of cloth-like deformable objects which we explore through a pulling interaction.
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space.
Batch Curation for Unsupervised Contrastive Representation Learning
The state-of-the-art unsupervised contrastive visual representation learning methods that have emerged recently (SimCLR, MoCo, SwAV) all make use of data augmentations in order to construct a pretext task of instant discrimination consisting of similar and dissimilar pairs of images.
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects.
Fashion Landmark Detection and Category Classification for Robotics
Research on automated, image based identification of clothing categories and fashion landmarks has recently gained significant interest due to its potential impact on areas such as robotic clothing manipulation, automated clothes sorting and recycling, and online shopping.
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects.
