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

2025-11-16

AI Summary

Complex embodied tasks—such as maze navigation and combination-lock manipulation—are difficult to model via conventional optimization due to their nonlinear, physics-coupled nature. Method: This paper introduces a code-based synthesis framework for designing nonlinear mechanical systems. It formalizes system topology, sensing, and actuation constraints using a Mechanical Description Language (MDL), then leverages large language models (LLMs) to map natural-language task specifications to executable MDL code. Integrating program synthesis algorithms, the framework automatically generates functional mass-spring networks exhibiting structural nonlinearity. Contribution/Results: This work is the first to unify MDL with LLM-driven code synthesis for end-to-end generation of physical structures from high-level task descriptions. Experimental validation demonstrates that synthesized systems autonomously execute high-order embodied behaviors—e.g., path planning and sequential state transitions—without external control signals, thereby establishing the feasibility of programmable embodied intelligence in purely passive mechanical architectures.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Intelligent Robots: Embodied AI
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Automating design of nonlinear mass-spring systems for specific tasks
• Overcoming optimization challenges in creating intelligent robotic behaviors
• Translating high-level code descriptions into functional mechanical networks

Innovation

• Code-based synthesis for mass-spring network design
• Automated generation from mechanical description language
• Integration with large-language models for natural-language input