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.

翻译：纸张是一种廉价、可回收且清洁的材料，常被用于制作实用工具。传统工具设计依赖于仿真或物理分析，这些方法往往不准确且耗时。本文提出PaperBot，一种无需人工干预即可直接在现实世界中利用纸张学习设计与使用工具的方法。我们在两项工具设计任务中验证了PaperBot的有效性与效率：1. 学习折叠并投掷纸飞机以实现最大飞行距离；2. 学习将纸张切割成夹爪以施加最大夹持力。我们提出一种自监督学习框架，通过学习执行折叠、切割及动态操作等一系列动作，以优化工具的设计与使用。我们将该系统部署于真实世界的双臂机器人系统上，以解决涉及空气动力学（纸飞机）与摩擦学（纸夹爪）的挑战性设计任务——这些任务无法通过仿真准确模拟。