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Humans excel in complex long-horizon soft body manipulation tasks viaflexible tool use: bread baking requires a knife to slice the dough and arolling pin to flatten it. Often regarded as a hallmark of human cognition,tool use in autonomous robots remains limited due to challenges inunderstanding tool-object interactions. Here we develop an intelligent roboticsystem, RoboCook, which perceives, models, and manipulates elasto-plasticobjects with various tools. RoboCook uses point cloud scene representations,models tool-object interactions with Graph Neural Networks (GNNs), and combinestool classification with self-supervised policy learning to devise manipulationplans. We demonstrate that from just 20 minutes of real-world interaction dataper tool, a general-purpose robot arm can learn complex long-horizon softobject manipulation tasks, such as making dumplings and alphabet lettercookies. Extensive evaluations show that RoboCook substantially outperformsstate-of-the-art approaches, exhibits robustness against severe externaldisturbances, and demonstrates adaptability to different materials.

RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools