Large Language Model Agents Enabled Generative Design of Fluidic Computation Interfaces

The creation of interactive devices is a major area of interest. However, traditional design tools in this field often require a significant learning curve and may not effectively support creative ideation. This study explores the use of fluidic computation interfaces as a case study to examine the potential of enhancing design tools for physical devices with Large Language Model (LLM) agents. With LLM agents, the Generative Design Tool (GDT) can understand the capabilities and limitations of new devices, suggest diverse, insightful, and practical application scenarios, and recommend designs that are technically and contextually appropriate. Additionally, it generates the necessary design parameters for the traditional components of the design tool to visualize results and create files for fabrication.

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Published
2024-10-11
DOI
https://doi.org/10.1145/3672539.3686351
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Large Language Model Agents Enabled Generative Design of Fluidic Computation Interfaces

Haipeng Mi, Yue Yang, Lining Yao, Jiawei Fang et al.
Modular Robots and Swarm Intelligence
article

Large Language Model Agents Enabled Generative Design of Fluidic Computation Interfaces

Haipeng Mi, Yue Yang, Lining Yao, Jiawei Fang, Qiuyu Lu, Shiqing Lyu, Zhihao Yao
article en

Abstract

The creation of interactive devices is a major area of interest. However, traditional design tools in this field often require a significant learning curve and may not effectively support creative ideation. This study explores the use of fluidic computation interfaces as a case study to examine the potential of enhancing design tools for physical devices with Large Language Model (LLM) agents. With LLM agents, the Generative Design Tool (GDT) can understand the capabilities and limitations of new devices, suggest diverse, insightful, and practical application scenarios, and recommend designs that are technically and contextually appropriate. Additionally, it generates the necessary design parameters for the traditional components of the design tool to visualize results and create files for fabrication.

Xiamen University (CN), Zhejiang University (CN), University of California, Berkeley (US), Tsinghua University (CN)
Openalex Percentile: Top 83%
Modular Robots and Swarm Intelligence
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Large Language Model Agents Enabled Generative Design of Fluidic Computation Interfaces — Haipeng Mi, Yue Yang, et al. · (2024) | TGRS Research Map | TGRS