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Tree of Thought Prompt in Robotic Arm Control

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Wireless Artificial Intelligent Computing Systems and Applications (WASA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14999))

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Abstract

The integration of Artificial Intelligence (AI) into robotic arms, particularly through the use of Large Language Models (LLMs), marks a significant advancement in automation, enabling these devices to perform complex tasks with unparalleled precision. Despite the potential of LLMs to revolutionize robotic functionality by understanding and processing human language, their application faces challenges, notably in generating precise, actionable commands for robotic operations. To address these issues, we introduce the “Tree of Thoughts” (ToT) methodology, enhanced by a Depth-First Search (DFS) strategy, designed to optimize LLM performance in robotic control. The ToT approach bridges the gap between LLMs’ abstract language comprehension and the generation of specific instructions, streamlining the command generation process and enabling robotic arms to execute tasks with increased precision and adaptability. This methodology not only demonstrates the feasibility of adapting LLMs as feedback controllers for complex tasks but also significantly improves the performance and adaptability of robotic arms, paving the way for more intelligent, responsive robotic systems.

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Author information

Authors and Affiliations

  1. Sam Houston State University, Huntsville, TX, USA

    Fan Liang

  2. Columbus State University, Columbus, GA, USA

    Lingqiang Ge

Authors
  1. Fan Liang
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  2. Lingqiang Ge
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Correspondence to Lingqiang Ge .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. Old Dominion University, Norfolk, VA, USA

    Daniel Takabi

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Shaoyong Guo

  4. Shandong University, Qingdao, China

    Yifei Zou

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Liang, F., Ge, L. (2025). Tree of Thought Prompt in Robotic Arm Control. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14999. Springer, Cham. https://doi.org/10.1007/978-3-031-71470-2_25

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  • DOI: https://doi.org/10.1007/978-3-031-71470-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71469-6

  • Online ISBN: 978-3-031-71470-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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