  Humans excel in complex long-horizon soft body manipulation tasks via
flexible tool use: bread baking requires a knife to slice the dough and a
rolling pin to flatten it. Often regarded as a hallmark of human cognition,
tool use in autonomous robots remains limited due to challenges in
understanding tool-object interactions. Here we develop an intelligent robotic
system, RoboCook, which perceives, models, and manipulates elasto-plastic
objects with various tools. RoboCook uses point cloud scene representations,
models tool-object interactions with Graph Neural Networks (GNNs), and combines
tool classification with self-supervised policy learning to devise manipulation
plans. We demonstrate that from just 20 minutes of real-world interaction data
per tool, a general-purpose robot arm can learn complex long-horizon soft
object manipulation tasks, such as making dumplings and alphabet letter
cookies. Extensive evaluations show that RoboCook substantially outperforms
state-of-the-art approaches, exhibits robustness against severe external
disturbances, and demonstrates adaptability to different materials.

Robotics