Learning to write with the fluid rope trick

TL;DR

This paper introduces a novel method using deep reinforcement learning to control viscous fluid jets, enabling precise pattern writing by harnessing the fluid rope trick for advanced 3D and 4D printing applications.

Contribution

It presents a new approach that combines deep reinforcement learning with fluid dynamics simulation to control viscous fluid streams for complex pattern deposition.

Findings

Successfully achieved cursive writing with viscous jets

Generated Pollock-like paintings using learned control strategies

Demonstrated real-world applicability in experimental setups

Abstract

The range and speed of direct ink writing, the workhorse of 3d and 4d printing, is limited by the practice of liquid extrusion from a nozzle just above the surface to prevent instabilities to cause deviations from the required print path. But what if could harness the ``fluid rope trick", whence a thin stream of viscous fluid falling from a height spontaneously folds or coils, to write specified patterns on a substrate? Using Deep Reinforcement Learning we control the motion of the extruding nozzle and thence the fluid patterns that are deposited on the surface. The learner (nozzle) repeatedly interacts with the environment (a viscous filament simulator), and improves its strategy using the results of this experience. We demonstrate the results in an experimental setting where the learned motion control instructions are used to drive a viscous jet to accomplish complex tasks such as cursive writing and Pollockian paintings.