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arXiv · 2306.14447

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

Haochen Shi·Huazhe Xu·Samuel Clarke·Yunzhu Li·Jiajun Wu
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Abstract

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.

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Haochen Shi, Huazhe Xu, Samuel Clarke, Yunzhu Li, Jiajun Wu. 2023-06-26. RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools. https://arxiv.org/abs/2306.14447

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