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GPT-enhanced robotic automation platform with user-friendly instruction framework for versatile applications

Zhitao Wang et al · Elsevier · 2026

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To address the challenges of hardware integration and system complexity in laboratory automation, this work introduces a universal platform built on two key innovations. First, a standardized instruction framework unifies the control of multi-brand robots by converting complex operations into simple, tabular instructions. Second, a zero-code natural language interface, powered by OpenAI’s GPTs platform, translates user commands into executable workflows, which are reviewed by trained domain scientists before execution, with an automated validation mechanism providing an additional safeguard. The platform’s performance was validated through complex, multi-device experiments, including an automated Cell Counting Kit-8 (CCK-8) cell viability assay, which yielded results highly consistent with those of manual operations. With a 99.0% success rate in translating natural language test instructions, this work demonstrates a practical framework to assist domain scientists in multi-robot laboratory automation.

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APA 7

al, Z. W. E. (2026). GPT-enhanced robotic automation platform with user-friendly instruction framework for versatile applications. https://doi.org/10.1016/j.slast.2026.100418

MLA

al, Zhitao Wang et. "GPT-enhanced robotic automation platform with user-friendly instruction framework for versatile applications." 2026. https://doi.org/10.1016/j.slast.2026.100418.

Chicago

al, Zhitao Wang et. 2026. "GPT-enhanced robotic automation platform with user-friendly instruction framework for versatile applications.". https://doi.org/10.1016/j.slast.2026.100418.

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al, Z. W. E. 2026, GPT-enhanced robotic automation platform with user-friendly instruction framework for versatile applications, Elsevier, available at: https://doi.org/10.1016/j.slast.2026.100418 [Accessed 7 Aug. 2026].

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Title
GPT-enhanced robotic automation platform with user-friendly instruction framework for versatile applications
Author / contributors
Zhitao Wang et al
Publisher
Elsevier
Publication year
2026
ISSN
2472-6303
ISSN
2472-6303
Language
English

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