2026-05-29原文

Decentralized LLM-Driven Coordination of Acoustic Robots for Contactless Object Manipulation

This paper presents a decentralized framework that combines large language models (LLMs) with acoustic mobile robots for contactless object manipulation. Using Whisper speech recognition, LLM semantic parsing, and JSON task scheduling, the system converts spoken commands into coordinated multi-robot actions. Experiments with two TurtleBot3-based acoustic robots achieved success rates of 96% for sequential, 86% for parallel, and 70% for synchronized tasks, showcasing the potential of LLM-driven automation for human-robot interaction.

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Key points

A decentralized framework integrates LLMs with acoustic robots for contactless object manipulation via natural language commands.
The system uses Whisper, LLM parsing, JSON-based task representation, and distributed scheduling to handle sequential, parallel, and synchronized tasks.
Experiments on two TurtleBot3 robots with ultrasonic arrays achieved 96% success for sequential, 86% for parallel, and 70% for synchronized operations.
The approach demonstrates potential for intuitive human-robot interaction in distributed robotic systems.

Why it matters

This matters because a decentralized framework integrates LLMs with acoustic robots for contactless object manipulation via natural language commands.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2605.29378] Decentralized LLM-Driven Coordination of Acoustic Robots for Contactless Object Manipulation

[Submitted on 28 May 2026]

Title:Decentralized LLM-Driven Coordination of Acoustic Robots for Contactless Object Manipulation

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Abstract:Natural language interfaces can simplify interaction with multi-robot systems, especially when non-expert users need to issue high-level commands. Acoustic manipulation using ultrasonic phased arrays also enables contactless object handling for applications such as healthcare, laboratory automation, and precision transport. However, combining large language models (LLMs) with distributed acoustic mobile robots remains underexplored. This paper presents a decentralized framework for natural language-driven coordination of acoustic robots for contactless object manipulation. The system converts spoken instructions into executable multi-robot task plans using Whisper-based speech recognition, LLM-based semantic parsing, structured JSON task representation, and distributed scheduling. The JSON schema encodes robot assignments, temporal dependencies, spatial constraints, and synchronization requirements for sequential, parallel, and synchronized execution. The system is implemented on two TurtleBot3-based acoustic robots, each equipped with an ultrasonic phased array for contactless object transport. Experiments were conducted in three scenarios: sequential execution, parallel multi-robot transport, and synchronized cooperative manipulation. The system achieved task success rates of 96 percent for sequential tasks, 86 percent for parallel execution, and 70 percent for synchronized collaborative transport. These results show that natural language commands can be transformed into distributed robot actions for contactless manipulation, highlighting the potential of LLM-driven automation for human-robot interaction in distributed robotic systems.

Comments: This paper has been accepted for publication in the Proceedings of the 2026 IEEE 22nd International Conference on Automation Science and Engineering (CASE 2026), August 17-21, 2026, Shenyang, China

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2605.29378 [cs.RO]

(or arXiv:2605.29378v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.29378

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Narsimlu Kemsaram Dr [view email] [v1] Thu, 28 May 2026 05:27:13 UTC (5,449 KB)

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