PaperBot: Learning to Design Real-World Tools Using Paper
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909137277812736 |
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| author | Liu, Ruoshi Liang, Junbang Sudhakar, Sruthi Ha, Huy Chi, Cheng Song, Shuran Vondrick, Carl |
| author_facet | Liu, Ruoshi Liang, Junbang Sudhakar, Sruthi Ha, Huy Chi, Cheng Song, Shuran Vondrick, Carl |
| contents | Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is often inaccurate and time-consuming. In this paper, we propose PaperBot, an approach that directly learns to design and use a tool in the real world using paper without human intervention. We demonstrated the effectiveness and efficiency of PaperBot on two tool design tasks: 1. learning to fold and throw paper airplanes for maximum travel distance 2. learning to cut paper into grippers that exert maximum gripping force. We present a self-supervised learning framework that learns to perform a sequence of folding, cutting, and dynamic manipulation actions in order to optimize the design and use of a tool. We deploy our system to a real-world two-arm robotic system to solve challenging design tasks that involve aerodynamics (paper airplane) and friction (paper gripper) that are impossible to simulate accurately. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09566 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PaperBot: Learning to Design Real-World Tools Using Paper Liu, Ruoshi Liang, Junbang Sudhakar, Sruthi Ha, Huy Chi, Cheng Song, Shuran Vondrick, Carl Robotics Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is often inaccurate and time-consuming. In this paper, we propose PaperBot, an approach that directly learns to design and use a tool in the real world using paper without human intervention. We demonstrated the effectiveness and efficiency of PaperBot on two tool design tasks: 1. learning to fold and throw paper airplanes for maximum travel distance 2. learning to cut paper into grippers that exert maximum gripping force. We present a self-supervised learning framework that learns to perform a sequence of folding, cutting, and dynamic manipulation actions in order to optimize the design and use of a tool. We deploy our system to a real-world two-arm robotic system to solve challenging design tasks that involve aerodynamics (paper airplane) and friction (paper gripper) that are impossible to simulate accurately. |
| title | PaperBot: Learning to Design Real-World Tools Using Paper |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.09566 |