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Instant-Fold addresses the high-dimensional complexity of deformable object manipulation by leveraging in-context imitation learning to execute diverse folding modes from a single demonstration without gradient updates. The framework combines deformation-aware visual representations with a flow-matching transformer policy, enabling zero-shot transfer from simulation to real-world environments. This approach offers a promising pathway for robotics systems to handle topology-changing interactions with minimal data collection.
Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
arXiv:2606.04269v1 Announce Type: new Abstract: Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes. We introduce Instant-Fold, an in-context imitation learning framework for DOM. Given a single human demonstration, our policy infers and executes diverse manipulation modes directly from the demonstration, including variations in spatial execution and ordering, without requiring gradient updates. Our approach first learns deformation-aware visual representations via temporal contrastive pretraining, after which a flow-matching transformer policy conditioned on the demonstration predicts actions to execute the intended manipulation mode. Trained entirely in simulation, Instant-Fold generalizes across diverse folding modes and transfers zero-shot to real-world settings without additional data collection or finetuning. Videos are available at https://instant-fold.github.io.
Abstract:Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes. We introduce Instant-Fold, an in-context imitation learning framework for DOM. Given a single human demonstration, our policy infers and executes diverse manipulation modes directly from the demonstration, including variations in spatial execution and ordering, without requiring gradient updates. Our approach first learns deformation-aware visual representations via temporal contrastive pretraining, after which a flow-matching transformer policy conditioned on the demonstration predicts actions to execute the intended manipulation mode. Trained entirely in simulation, Instant-Fold generalizes across diverse folding modes and transfers zero-shot to real-world settings without additional data collection or finetuning. Videos are available at this https URL.
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2606.04269 [cs.RO]
(or arXiv:2606.04269v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.04269 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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