PaperBot: Learning to Design Real-World Tools Using Paper

TL;DR

PaperBot introduces a self-supervised learning approach enabling robots to design and utilize paper-based tools for tasks like flying paper airplanes and gripping, bypassing simulation inaccuracies.

Contribution

It presents a novel real-world, self-supervised learning framework for robotic tool design using paper, eliminating the need for simulation or human intervention.

Findings

Successfully designed paper airplanes with maximum travel distance

Created effective paper grippers with high gripping force

Demonstrated real-world applicability on robotic systems

Abstract

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.