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

• URL: http://arxiv.org/abs/2404.12524v1
• Date: Thu, 18 Apr 2024 21:55:23 GMT
• Title: DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects
• Authors: Dominik Bauer, Zhenjia Xu, Shuran Song,
• Abstract summary: Transformer-based architecture for planning interactions with elastoplastic objects. DoughNet allows to plan robotic manipulation; selecting a suited tool, its pose and opening width to recreate robot- or human-made goals.
• Score: 27.194896819729113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 to determine long-horizon geometrical deformation and topological changes purely in latent space. Given a partial initial state and desired manipulation trajectories, it infers all resulting object geometries and topologies at each step. DoughNet thereby allows to plan robotic manipulation; selecting a suited tool, its pose and opening width to recreate robot- or human-made goals. Our experiments in simulated and real environments show that DoughNet is able to significantly outperform related approaches that consider deformation only as geometrical change.

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