DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects

arXiv:2404.12524v1 Announce Type: cross
Abstract: Manipulation of elastoplastic objects like dough often involves topological changes such as splitting and merging. The ability to accurately predict these topological changes that a specific action might incur is critical for planning interactions with elastoplastic objects. We present DoughNet, a Transformer-based architecture for handling these challenges, consisting of two components. First, a denoising autoencoder represents deformable objects of varying topology as sets of latent codes. Second, a visual predictive model performs autoregressive set prediction …

abstract architecture arxiv cs.cv cs.lg cs.ro interactions manipulation merging objects planning predictive transformer type visual