DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects
arxiv(2024)
摘要
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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