Synthesis of mass-spring networks from high-level code descriptions

TL;DR

This paper introduces a code-based method to automatically synthesize mass-spring networks for robotic functions from high-level descriptions, enabling complex behaviors like maze navigation and programmable locks.

Contribution

It presents a novel synthesis approach using a mechanical description language and software to generate functional mass-spring systems from high-level code descriptions.

Findings

Successfully designed a maze-navigating robot using the method.

Created a programmable lock system from high-level descriptions.

Demonstrated the integration of natural language with mechanical synthesis.

Abstract

Structural nonlinearity can be harnessed to program complex functionalities in robotic devices. However, it remains a challenge to design nonlinear systems that will accomplish a specific, desired task. The responses that we typically describe as intelligent -- such a robot navigating a maze -- require a large number of degrees of freedom and cannot be captured by traditional optimization objective functions. In this work, we explore a code-based synthesis approach to design mass-spring systems with embodied intelligence. The approach starts from a source code, written in a \emph{mechanical description language}, that details the system boundary, sensor and actuator locations, and desired behavior. A synthesizer software then automatically generates a mass-spring network that performs the described function from the source code description. We exemplify this methodology by designing mass-spring systems realizing a maze-navigating robot and a programmable lock. Remarkably, mechanical description languages can be combined with large-language models, to translate a natural-language description of a task into a functional device.