  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.
