PaperBot: Learning to Design Real-World Tools Using Paper

TL;DR

This paper proposes PaperBot, an approach that directly learns to design and use a tool in the real world using paper without human intervention, and presents 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.

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.

Figures (11)

• Figure 1: Self-supervised Design of Paper Tools. Through trial-and-error, PaperBot autonomously learns how to design and use paper tools directly in the real world. Given only 100 trials ($\approx$ 3 hours), our fully autonomous system discovers a paper airplane folding and throwing strategy that flew further than the best human design after the same number of trials (top), and learns how to cut and actuate a kirigami gripper that exerts 0.93N of force (bottom), equivalent to the weight of over four strawberries. For our system's final design, please see Fig. \ref{['fig:parameterization']}.
• Figure 2: Paper Tools. Paper is an affordable and versatile medium for constructing a variety of different paper tools.
• Figure 3: Approach Overview. Our framework samples paper designs for a tool, builds them, actuates a robot to perform on a task with the tool, and perceives its performance. By learning a surrogate model to predict the utility of a design, we obtain a differentiable model that allows us to solve inverse design tasks with gradient-based optimization. The above figure shows how our framework applies to two design tasks of paper airplanes (top) and kirigami grippers (bottom).
• Figure 4: System Setup. We visualize the workspace of PaperBot while designing paper airplanes (a-c) and kirigami grippers (d-e). (a) shows the full workspace, including two RealSense D435i cameras to measure travel distance over the runway. (b) shows two xArm7s folding a paper airplane. (c) shows the sticky holders. (d) shows the xArm 7s retrieving a gripper cut by the Cricut Maker 3 and actuating it. (e) shows load cells that we use to measure the force of the candidate gripper design.
• Figure 5: Parameterization and Learning Process. Left shows the parameterization of the tools. Right shows the intermediate designs during the learning process as well as the best human-fold plane and adapted gripper designs for smaller and larger objects. These iterations correspond to the teaser figure. See Fig. \ref{['fig:teaser']} for the actual performance of these designs.