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REVIEW 2 major objections 30 references

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

T0 review · 2 major / 0 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read A decentralized framework converts natural language commands into executable plans for multiple acoustic robots to perform contactless object manipulation.

desk verdict The paper builds a working pipeline from speech to acoustic robot tasks but the decentralization claim does not hold because the LLM parsing step sits upstream of any distribution. read the letter →

arxiv 2605.29378 v1 pith:W6QZXPT7 submitted 2026-05-28 cs.RO

Yingying Wang , Narsimlu Kemsaram , Sriram Subramanian This is my paper · ORCID
classification cs.RO
keywords decentralizedmulti-robotcoordinationlargelanguagemodelsacousticmanipulationcontactlessobjecttransportnaturalinterfacesdistributedtaskschedulingultrasonicphasedarraysplanning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents a system that takes spoken instructions and turns them into coordinated actions for acoustic robots equipped with ultrasonic arrays to move objects without touching them. Speech is recognized and parsed by an LLM into a structured task format that encodes assignments, timing, space limits, and synchronization needs, then executed via distributed scheduling on the robots. Tests on two physical robots covered sequential tasks, parallel moves, and synchronized transport, with reported success rates of 96 percent, 86 percent, and 70 percent respectively. A sympathetic reader would care because this removes the need for central control or expert programming, opening natural-language access to multi-robot contactless handling in settings such as labs or medical transport.

What carries the argument

The structured JSON task representation that encodes robot assignments, temporal dependencies, spatial constraints, and synchronization requirements, enabling distributed scheduling without central oversight.

What would settle it

Re-running the three scenarios with new natural-language phrasings or added environmental variations such as obstacles and checking whether success rates fall substantially below the reported 70-96 percent range.

Watch

Extended reading notes

Core claim

Natural language commands can be transformed into distributed robot actions for contactless manipulation by using LLM-based semantic parsing to generate a JSON task schema that encodes robot assignments, temporal dependencies, spatial constraints, and synchronization requirements, followed by decentralized scheduling; this produces executable plans that achieve 96 percent success on sequential tasks, 86 percent on parallel execution, and 70 percent on synchronized collaborative transport when implemented on two TurtleBot3-based acoustic robots.

Load-bearing premise

The LLM-based semantic parsing and distributed scheduling produce correct executable plans from natural language without central oversight, and the reported success rates reflect reliable real-world performance rather than idealized test conditions.

Editorial extensions

If this is right

  • High-level spoken commands suffice to direct sequential, parallel, and synchronized multi-robot acoustic tasks.
  • Contactless object transport can be achieved without physical grippers or central coordination.
  • The JSON schema supports temporal and spatial constraints that keep distributed execution feasible.
  • Success rates above 70 percent hold across the three tested execution modes on physical hardware.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extending the same JSON schema to teams larger than two robots would test whether decentralized scheduling scales without added latency.
  • Replacing the current speech recognizer with alternatives could reveal how much the overall pipeline depends on accurate initial transcription.
  • Applying the framework to tasks that mix acoustic manipulation with other robot capabilities would check cross-modal compatibility.
  • Measuring energy use or array power during synchronized moves could quantify practical limits for battery-powered deployments.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The manuscript presents a decentralized framework for natural language-driven coordination of acoustic robots for contactless object manipulation. Spoken commands are converted via Whisper speech recognition and LLM semantic parsing into structured JSON task plans (encoding assignments, dependencies, and synchronization), followed by distributed scheduling. The system is implemented on two TurtleBot3 robots with ultrasonic phased arrays; experiments report success rates of 96% (sequential), 86% (parallel), and 70% (synchronized collaborative transport).

Significance. If the reported success rates are supported by proper trial counts, statistics, and failure analysis, and if the decentralization claim can be reconciled with the architecture, the work would demonstrate a practical integration of LLMs with distributed acoustic manipulation, offering a concrete example of natural-language interfaces for multi-robot contactless tasks in domains such as laboratory automation.

major comments (2)
  1. [Abstract] Abstract: the title and abstract repeatedly describe the system as providing 'decentralized LLM-driven coordination,' yet the architecture isolates LLM-based semantic parsing (and JSON generation) as a prerequisite step performed before any distributed scheduling; this creates an unaddressed central point of failure that directly undercuts the decentralization claim.
  2. [Abstract] Abstract: the success rates (96%, 86%, 70%) are presented without any accompanying trial counts, statistical measures, confidence intervals, error analysis, or description of failure modes, rendering it impossible to assess whether the metrics support the coordination claims under realistic conditions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We respond to each major comment below and indicate where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the title and abstract repeatedly describe the system as providing 'decentralized LLM-driven coordination,' yet the architecture isolates LLM-based semantic parsing (and JSON generation) as a prerequisite step performed before any distributed scheduling; this creates an unaddressed central point of failure that directly undercuts the decentralization claim.

    Authors: The referee is correct that LLM-based semantic parsing occurs as a centralized preprocessing step to produce the structured JSON task plan before distributed scheduling begins. The decentralization claim in the manuscript refers specifically to the multi-robot scheduling and execution phase, which proceeds without a central controller once the plan is generated. We agree this distinction is not sufficiently clear in the current abstract and title. We will revise both to state that the system uses LLM-driven parsing to enable subsequent decentralized coordination, thereby addressing the concern about a central point of failure. revision: yes

  2. Referee: [Abstract] Abstract: the success rates (96%, 86%, 70%) are presented without any accompanying trial counts, statistical measures, confidence intervals, error analysis, or description of failure modes, rendering it impossible to assess whether the metrics support the coordination claims under realistic conditions.

    Authors: We agree that the abstract presents the success rates without the supporting details the referee requests. The full experimental section of the manuscript describes the trial counts, basic statistics, and failure modes (such as positioning drift and timing mismatches). To make the abstract more informative, we will add a concise statement on the number of trials performed per scenario and note that detailed failure analysis appears in the experiments section. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: system description with experimental results only

full rationale

The paper describes an implemented multi-robot system (Whisper + LLM parsing to JSON plans, followed by distributed scheduling on TurtleBot3 acoustic robots) and reports measured success rates (96/86/70 %) from three experimental scenarios. No equations, derivations, fitted parameters, or predictions are present. No self-citations are invoked to justify any load-bearing claim. The architecture and results are externally falsifiable via replication on the described hardware; nothing reduces to its own inputs by construction.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No mathematical model, free parameters, axioms, or invented entities; the contribution is an engineering integration of off-the-shelf technologies.

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Cite this review

Pith. "Pith review of Decentralized LLM-Driven Coordination of Acoustic Robots for Contactless Object Manipulation." pith.science (2026). https://pith.science/paper/W6QZXPT7

@misc{pith2026260529378,
  author       = {Pith},
  title        = {Pith review of: Decentralized LLM-Driven Coordination of Acoustic Robots for Contactless Object Manipulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W6QZXPT7}},
  note         = {Machine review of arXiv:2605.29378}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2605.29378 by the authors.

Figure 1
Figure 1. Proposed decentralized LLM-driven coordination of acoustic robots [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Architecture of the proposed decentralized natural language–driven multi-agent framework. Spoken user commands are first transcribed by the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Experimental setup: Hardware components of the acoustic robot [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Natural language–driven multi-robot coordination scenarios for [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Sequential task execution scenario used for experimental evaluation [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 9
Figure 9. Figure 9: Synchronous collaborative task execution (Scenario 3): Two [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Synchronous acoustic levitation stability during cooperative multi [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]

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Reference graph

Works this paper leans on

30 extracted references · 3 canonical work pages

  1. [1]

    Cooperative heterogeneous multi-robot systems: A survey,

    Y . Rizk, M. Awad, and E. W. Tunstel, “Cooperative heterogeneous multi-robot systems: A survey,”ACM Computing Surveys (CSUR), vol. 52, no. 2, pp. 1–31, 2019

    2019

  2. [2]

    Multi-agent robot systems as distributed autonomous systems,

    J. Ota, “Multi-agent robot systems as distributed autonomous systems,” Advanced engineering informatics, vol. 20, no. 1, pp. 59–70, 2006

    2006

  3. [3]

    A survey and analysis of multi-robot coordination,

    Z. Yan, N. Jouandeau, and A. A. Cherif, “A survey and analysis of multi-robot coordination,”International Journal of Advanced Robotic Systems, vol. 10, no. 12, p. 399, 2013

    2013

  4. [4]

    A review of research in multi-robot systems,

    A. Gautam and S. Mohan, “A review of research in multi-robot systems,” in2012 IEEE 7th international conference on industrial and information systems (ICIIS). IEEE, 2012, pp. 1–5

    2012

  5. [5]

    Constraint-driven coordinated control of multi-robot systems,

    G. Notomista and M. Egerstedt, “Constraint-driven coordinated control of multi-robot systems,” in2019 American Control Conference (ACC), 2019, pp. 1990–1996

    2019

  6. [6]

    Coordinated control of multi-robot systems: A survey,

    J. Cort ´es and M. Egerstedt, “Coordinated control of multi-robot systems: A survey,”SICE Journal of Control, Measurement, and System Integration, vol. 10, no. 6, pp. 495–503, 2017

    2017

  7. [7]

    Lami: Large language models for multi-modal human-robot interaction,

    C. Wang, S. Hasler, D. Tanneberg, F. Ocker, F. Joublin, A. Ceravola, J. Deigmoeller, and M. Gienger, “Lami: Large language models for multi-modal human-robot interaction,” inExtended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024, pp. 1–10

    2024

  8. [8]

    Advances in large language models for robotics,

    Z. Qi and X. Jing, “Advances in large language models for robotics,” in2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA). IEEE, 2024, pp. 72–76

    2024

Show all 30 references
  1. [9]

    Large language models for robotics: A survey,

    F. Zeng, W. Gan, Y . Wang, N. Liu, and P. S. Yu, “Large language models for robotics: A survey,”arXiv preprint arXiv:2311.07226, 2023

    2023

  2. [10]

    Breaking the code barrier: Analyzing the effectiveness of llms in natural language interfaces for collaborative robotics,

    J. Wolfartsberger and D. Niedermayr, “Breaking the code barrier: Analyzing the effectiveness of llms in natural language interfaces for collaborative robotics,” in2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC). IEEE, 2025, pp. 274–280

    2025

  3. [11]

    Large language models for multi- robot systems: A survey,

    P. Li, Z. An, S. Abrar, and L. Zhou, “Large language models for multi- robot systems: A survey,”arXiv preprint arXiv:2502.03814, 2025

    2025 arXiv

  4. [12]

    Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?

    Y . Chen, J. Arkin, Y . Zhang, N. Roy, and C. Fan, “Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 4311–4317

    2024

  5. [13]

    A text-book of physics—sound. by jh poynting and jj thomson. london, charles. griffin & co. 1899. pp. 163

    W. L. Stevens, “A text-book of physics—sound. by jh poynting and jj thomson. london, charles. griffin & co. 1899. pp. 163.”Science, vol. 9, no. 234, pp. 872–874, 1899

  6. [14]

    Three-dimensional mid-air acoustic manipulation by ultrasonic phased arrays,

    Y . Ochiai, T. Hoshi, and J. Rekimoto, “Three-dimensional mid-air acoustic manipulation by ultrasonic phased arrays,”PloS one, vol. 9, no. 5, p. e97590, 2014

    2014

  7. [15]

    Contactless acoustic manipulation and sorting of particles by dynamic acoustic fields,

    M. A. Andrade, G. D. Skotis, S. Ritchie, D. R. Cumming, M. O. Riehle, and A. L. Bernassau, “Contactless acoustic manipulation and sorting of particles by dynamic acoustic fields,”IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 63, no. 10, pp. 1593...

    2016

  8. [16]

    Contact-less manipulation of millimeter-scale objects via ultrasonic levitation,

    J. Nakahara, B. Yang, and J. R. Smith, “Contact-less manipulation of millimeter-scale objects via ultrasonic levitation,” in2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2020, pp. 264–271

    2020

  9. [17]

    Dynamic multi-robot task allocation under uncertainty and temporal constraints,

    S. Choudhury, J. K. Gupta, M. J. Kochenderfer, D. Sadigh, and J. Bohg, “Dynamic multi-robot task allocation under uncertainty and temporal constraints,”Autonomous Robots, vol. 46, no. 1, pp. 231–247, 2022

    2022

  10. [18]

    Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,

    O. Friha, M. A. Ferrag, B. Kantarci, B. Cakmak, A. Ozgun, and N. Ghoualmi-Zine, “Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,” IEEE Open Journal of the Communications Society, vol. 5, pp. 5799– 5856, 2024

    2024

  11. [19]

    Distributed phased arrays: Challenges and recent advances,

    J. A. Nanzer, S. R. Mghabghab, S. M. Ellison, and A. Schlegel, “Distributed phased arrays: Challenges and recent advances,”IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 11, pp. 4893–4907, 2021

    2021

  12. [20]

    Structured learning for spoken language understanding in human- robot interaction,

    E. Bastianelli, G. Castellucci, D. Croce, R. Basili, and D. Nardi, “Structured learning for spoken language understanding in human- robot interaction,”The International journal of robotics research, vol. 36, no. 5-7, pp. 660–683, 2017

    2017

  13. [21]

    A survey on integration of large language models with intelligent robots,

    Y . Kim, D. Kim, J. Choi, J. Park, N. Oh, and D. Park, “A survey on integration of large language models with intelligent robots,” Intelligent Service Robotics, vol. 17, no. 5, pp. 1091–1107, 2024

    2024

  14. [22]

    Enhancing the llm-based robot manipulation through human-robot collaboration,

    H. Liu, Y . Zhu, K. Kato, A. Tsukahara, I. Kondo, T. Aoyama, and Y . Hasegawa, “Enhancing the llm-based robot manipulation through human-robot collaboration,”arXiv preprint arXiv:2406.14097, 2024

    2024

  15. [23]

    Centralized llm-driven multi-robot coordination for cooperative ob- ject transportation,

    J. Xie, W. Zhang, H. Chen, J. Zeng, Y . Gao, Z. Xu, and K. Hashimoto, “Centralized llm-driven multi-robot coordination for cooperative ob- ject transportation,” in2025 IEEE International Conference on Ad- vanced Robotics and its Social Impacts (ARSO). IEEE, 2025, pp. 368–373

    2025

  16. [24]

    Decentralized task and path planning for multi-robot systems,

    Y . Chen, U. Rosolia, and A. D. Ames, “Decentralized task and path planning for multi-robot systems,”IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4337–4344, 2021

    2021

  17. [25]

    Non-contact handling in microassembly: Acoustical levitation,

    V . Vandaele, P. Lambert, and A. Delchambre, “Non-contact handling in microassembly: Acoustical levitation,”Precision engineering, vol. 29, no. 4, pp. 491–505, 2005

    2005

  18. [26]

    A non- contact manipulation for robotic applications: a review on acoustic levitation,

    I. I. I. Al-Nuaimi, M. N. Mahyuddin, and N. K. Bachache, “A non- contact manipulation for robotic applications: a review on acoustic levitation,”IEEE Access, vol. 10, pp. 120 823–120 837, 2022

    2022

  19. [27]

    Acoustophoretic contactless transport and handling of matter in air,

    D. Foresti, M. Nabavi, M. Klingauf, A. Ferrari, and D. Poulikakos, “Acoustophoretic contactless transport and handling of matter in air,” Proceedings of the National Academy of Sciences, vol. 110, no. 31, pp. 12 549–12 554, 2013

    2013

  20. [28]

    Ultraino: An open phased-array system for narrowband airborne ultrasound transmis- sion,

    A. Marzo, T. Corkett, and B. W. Drinkwater, “Ultraino: An open phased-array system for narrowband airborne ultrasound transmis- sion,”IEEE Transactions on Ultrasonics, Ferroelectrics, and Fre- quency Control, vol. 65, no. 1, pp. 102–111, 2018

    2018

  21. [29]

    Holograms for acoustics,

    K. Melde, A. G. Mark, T. Qiu, and P. Fischer, “Holograms for acoustics,”Nature, vol. 537, no. 7621, pp. 518–522, 2016

    2016

  22. [30]

    Acoustic levitation in mid-air: Recent advances, challenges, and future perspectives,

    M. A. Andrade, A. Marzo, and J. C. Adamowski, “Acoustic levitation in mid-air: Recent advances, challenges, and future perspectives,” Applied Physics Letters, vol. 116, no. 25, 2020

    2020

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Reviewed June 29, 2026 · model on record in the stance chip above.