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REVIEW 3 major objections 5 minor 39 references

Deployment Is Not Destiny: Robot Recomposition in the Field with Unseen Software, Hardware, and Compute Payloads

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A runtime framework lets deployed robots adopt new software, hardware, and compute payloads on the fly, sharing them with peers, and reconfigures in minutes without developer intervention.

desk verdict Real systems contribution with field demos, but 'no developer intervention' hides a packaging contract that relocates rather than eliminates the developer work. read the letter →

arxiv 2608.11063 v1 pith:YPO3MHU7 submitted 2026-08-11 cs.RO

Steven Swanbeck , Jonathan Salfity , Jeffery Gunawan , Corrie Van Sice , Mitch Pryor , Robert Blake Anderson This is my paper · ORCID
classification cs.RO
keywords runtimerobotrecompositionnondumvisumpayloadsplug-and-playhardwareandsoftwarecomponentmanagerbehaviortreesPDDLtaskplanningdistributedcapabilitysharingdisasterresponserobotics
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

This paper argues that field robots need not stay locked in their factory configuration. It presents a framework that lets a deployed robot discover, install, and use software, hardware, and compute payloads it has never seen before, all while running and without a developer. New resources immediately become available to the host robot and are shared with other robots on the network, so a compute-limited robot can borrow a GPU-equipped peer's capability. The authors demonstrate the system in two disaster-response exercises, reporting reconfiguration times of roughly one to seven minutes. The bet is that careful packaging abstractions can turn robotic integration from an expert, hours-long task into a plug-and-play act.

What carries the argument

The load-bearing object is the Component Manager paired with a strict component contract. Every software payload must be a Docker-container image compiled for the host CPU plus a keyword-indexed relational database containing behavior and interface libraries for each CPU architecture and PDDL action and domain fragments; hardware payloads must be USB devices that fire udev events; compute payloads must run a peer-discovery and RPC daemon on a shared subnet. The Component Manager reacts to these three update types, verifies resources, instantiates containers, maps devices into them, and shares databases with peers. On top of that, a system-level behavior tree monitors for updates, rebuilds the world model from all component databases, translates natural-language goals into PDDL, and generates a task-level behavior tree that composes the available behaviors into an executable plan. The machinery thus converts plug-in events into fresh planning knowledge and executable control without a human writing code.

What would settle it

Take an arbitrary off-the-shelf sensor that connects over Ethernet or a serial port, or any software tool not wrapped in the required container-plus-database format, and plug it into a robot running the framework; if the robot cannot discover, install, and plan with it without developer intervention, the universal 'previously unseen payload' claim fails. A lighter-weight check is to measure recomposition time when a non-expert must also supply planning metadata, since the framework assumes the database already encodes the payload's behaviors and planning fragments.

Watch

Extended reading notes

Core claim

The paper's central claim is that open-ended runtime recomposition is achievable: a robot can integrate nondum visum payloads—self-contained software, hardware, or compute resources unknown at deployment time—through a true plug-and-play process, with no developer intervention and no reboot. The framework unifies each payload type under composable abstractions: software arrives as a compiled container image plus a keyword-indexed relational database carrying behavior libraries and PDDL planning fragments; hardware arrives as USB devices that trigger udev events; compute peers arrive over the network through a P2P/RPC daemon. A Component Manager watches for these local updates, installs and verifies payloads, and forwards their databases to peers. Task goals are translated from natural language into PDDL, planned over the union of all known component databases, then rendered as a task-level behavior tree that is executed by the running system. Two demonstrations—radioactive source localization in a nuclear facility and a thermal-guided person search—show payloads swapped between stages in 1m33s to 7m22s, reused across three host robots and an external GPU compute unit.

Load-bearing premise

The whole scheme depends on every software payload arriving pre-packaged as a container image plus a keyword-indexed database of behaviors, interfaces, and PDDL fragments for the host CPU, and every hardware payload being a USB device that fires udev events; a payload that does not fit this wrapper can never be discovered or composed.

Editorial extensions

If this is right

  • A robot in the field can be retasked for a new mission by plugging in a payload and stating a goal, without waiting for experts to recompile or relaunch software.
  • Capabilities introduced on one robot propagate to its network peers, so a small, compute-constrained robot can plan and execute using a remote GPU or sensor.
  • The same payload can be reused across different host robots and environments, so investment in payload development is amortized over heterogeneous fleets.
  • Reconfiguration time drops from the order of hours of expert engineering to minutes of non-expert action, changing how robots can be staged for emergencies.
  • Robots can sequence several independently designed payloads into a single coherent task plan, because each contributes PDDL fragments and behavior entries.

Reading between the lines

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

  • If the packaging contract becomes a community standard, the framework predicts an ecosystem where payload vendors ship a container plus planning metadata and any compliant robot can adopt the capability with no integration testing—an implicit claim that planning-level description is sufficient to substitute for hand-tuned integration parameters.
  • A stress test the paper does not report is simultaneous hot-plugging and removal of many payloads or payload failure mid-plan; the reactive potential is currently capped by the classical PDDL planner, as the authors note, so behavior-tree-native synthesis would be needed before truly reactive recomposition.
  • The USB-only hardware path suggests an immediate extension: network-attached sensors and actuators would require the peer daemon or a different discovery trigger, and it is an open question whether latencies and reliability would hold.
  • The reliance on human teammates for payload selection implies the next bottleneck is not integration time but operator judgment about which payload fits the task and environment.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents a framework for runtime robot recomposition that allows new software, hardware, and compute payloads to be discovered, loaded, and composed into task plans without rebooting or reprogramming. The approach combines a component manager, USB/udev-based hardware detection, peer-to-peer compute sharing, Docker containers, behavior trees, and a PDDL planner with an LLM-based natural language interface. The authors demonstrate the framework on three host robots in two scenarios: radioactive source localization at a nuclear reactor facility and a thermal-guided search for people in a dark building. They claim integration of 'previously unseen' payloads in minutes with 'no developer intervention' by non-expert users, and automatic sharing of new capabilities with distributed peers. The demonstrations are real, but the claims are broader than the evidence: the module contract in Section III-A requires payloads to be pre-packaged by developers, and the evaluation is single-shot with no baselines, repetitions, or external user trials.

Significance. If the central claims hold, the framework would be a meaningful step toward field-adaptable robots, particularly the ability to share new sensing and compute capabilities between heterogeneous robots. Strengths include physical demonstrations at an operational nuclear facility and a realistic dark-building search, reuse of identical payloads across platforms (Fig. 6), and a concrete integration of discovery, planning, and execution in one pipeline. The paper also clearly identifies a gap in existing modular robotics work regarding runtime discovery and automatic sharing. However, the current evidence does not support the strong 'no developer intervention' and 'non-expert' claims, and the lack of baselines and error bars prevents quantitative validation of the headline timescale improvement.

major comments (3)
  1. [Section III-A and Fig. 4/5] The abstract's claim of 'no developer intervention' is not supported by the evidence. The module contract in Section III-A requires every software payload to arrive as a [14]-compliant compiled container image plus a keyword-indexed relational database with architecture-specific behavior and interface libraries and PDDL fragments. Authoring these artifacts is precisely the kind of developer work the abstract says is unnecessary, and all payloads in Fig. 3 were prepared by the authors' team. The recomposition times in Fig. 4 (3m19s, 2m16s, 1m33s) and Fig. 5 (5m39s, 7m22s) begin after those packages exist and include manual plugging by a teammate. The demonstrations therefore validate discovery, loading, planning, and execution of pre-packaged components, not non-expert integration of a genuinely new off-the-shelf payload. I recommend softening the claim to 'no developer intervention during runtime' or adding an experiment where a third-party user, not the authors, packages and integrates a payload.
  2. [Section IV-A and IV-B] The evaluation is single-shot and lacks baselines or error bars. Each recomposition time is reported as a single number for each stage, with no repeated trials, no variance, and no comparison condition against, for example, a traditional manual reintegration or a reboot-based approach. The paper states a contrast with 'hours of expert effort' (Section IV-C) but provides no measurement of that baseline. Additionally, the claim that 'non-roboticist end users' can perform these reconfigurations is not tested: the teammate in the demonstrations appears to be part of the research team, and no user study or independent operator is described. These omissions matter because the central contribution is a time- and expertise-based claim. Adding repeated runs and at least one external operator trial would substantially strengthen the paper.
  3. [Section III-A, III-B, and V] The scope of 'previously unseen' payloads is narrower than the text suggests. Hardware payloads are required to be USB devices that fire udev events (Section III-B2), compute peers must run the same P2P/RPC daemon on a predetermined subnet (Section III-A), and software payloads must be pre-packaged in the [14]-compliant format described above. The framework is therefore not open to arbitrary payloads; it is open to payloads that conform to a specific wrapper contract. The paper's own Limitations section (Section V) concedes that payload selection and physical mounting remain human responsibilities. This is not a fatal flaw, but the phrasing 'previously unseen modular software, hardware, and compute payloads' and 'true plug-and-play' should be qualified so that the reader understands the wrapper is part of the system.
minor comments (5)
  1. [Section V] There is a typo: 'there no is verification' should be 'there is no verification'.
  2. [Section I] The term 'nondum visum' is used without definition or translation; define it at first use (e.g., 'previously unseen') to make the paper accessible.
  3. [Table I] The grid is a useful summary, but the characterization of prior work, particularly [17] and [7], should be double-checked against the cited papers, since the row comparisons hinge on what counts as 'auto composed' versus 'auto shared.' Clarify the criteria in the caption.
  4. [Figures 4 and 5] The captions are dense and the tags are difficult to parse; consider a legend or a more explicit timeline, and state that times are single measurements unless trials are added.
  5. [Section VI] The conclusion repeats the abstract's wording ('truly plug-and-play modules'); align the conclusion with the qualified claims after revision.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor self-citation to the authors' own CORAL abstraction bounds the 'previously unseen' software claim; no mathematical derivation reduces to its inputs.

  1. self citation load bearing [Section III-A 'Component Design and Abstractions' (software abstraction paragraph); abstract 'no developer intervention' claim.]
    "To overcome these limitations, our abstraction requires software components to provide (i) [14]-compliant compiled images and (ii) a corresponding keyword-indexed relational database containing low-level compiled behavior and interface libraries for various CPU architectures and high-level PDDL [34] actions and domain and problem fragments corresponding to the new payload’s afforded behaviors."

    The software payload contract is defined in terms of the authors' own prior [14] format: a payload is 'integrable' iff it is a [14]-compliant image plus a database of behavior/interface libraries and PDDL fragments. Since [14] is the same team's earlier CORAL paper, the load-bearing standard is self-citation, and every demonstrated payload was pre-authored to that standard by the project team. The measured minutes-scale recomposition time therefore begins after the compiled image, libraries, and PDDL fragments already exist, so the 'no developer intervention' headline excludes the developer effort of authoring those artifacts.

full rationale

The paper's central claim is an engineering capability, not a derived prediction: the framework is implemented and exercised in two physical demonstrations with three host robots, timing measurements, and task-level behavior-tree execution. Those demonstrations provide external, non-circular evidence that the runtime recomposition machinery works. The only circularity-adjacent element is the software module contract of Section III-A, which requires every software payload to arrive as a [14]-compliant Docker image plus a database of architecture-specific libraries and PDDL fragments. [14] is the authors' own prior CORAL work, and all demonstrated payloads are pre-built to that standard. Consequently, 'previously unseen' is true relative to the robot at runtime but not relative to the development process, and 'no developer intervention' excludes the authoring of the package. The paper itself acknowledges this boundary in Section V: 'our scope focuses specifically on the scheduling, information flow, and utilization of dynamically introduced payloads.' Because the contribution does not reduce by equation to its inputs, and because the cited prior work is one component of a larger implemented system rather than a uniqueness theorem forbidding alternatives, the appropriate score is low.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on framework-specific contracts rather than fitted numerical parameters, so the free-parameter ledger is empty. The important assumptions are the component packaging contract, USB and udev hardware discovery, trusted peer infrastructure, and the planning-sufficiency assumption that leaves payload selection to humans. These assumptions are explicit in the text and are the main reason the 'open module set' claim is narrower than a literal reading might suggest.

assumptions (4)
  • ad hoc to paper Every software payload must be pre-packaged as a [14]-compliant compiled container image plus a keyword-indexed relational database containing behavior libraries, interface libraries, and PDDL fragments for the host architecture.
    This custom contract is the entry ticket to the open module set. Payloads that do not ship with this wrapper cannot be discovered or composed, so the 'unseen payload' claim is scoped to conforming components (Section III-A).
  • domain assumption Hardware payloads must be USB devices that produce udev events and be accompanied by a concurrently introduced software component that maps the device node into a container.
    Hardware discovery relies on the Linux USB and udev stack; non-USB or non-udev devices and devices without bundled drivers are outside the demonstrated capability (Section III-B).
  • domain assumption Distributed peers run the same Component Manager and P2P and RPC daemon on a predetermined subnet, discover each other by UDP multicast, and are trusted with component databases.
    Distributed capability sharing presupposes preinstalled peer software, network reachability, and trust among robots; this is not automatic for arbitrary third-party computers (Sections III-A and III-B).
  • domain assumption At planning time the robot assumes its current payload suite is sufficient for the provided goal; payload selection, task alignment, and mounting correctness are the human teammate's responsibility.
    Acknowledged in the Limitations as outside scope. This keeps the framework honest but narrows the autonomy claim: the human still chooses and mounts the right payloads (Section V).

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Cite this review

Pith. "Pith review of Deployment Is Not Destiny: Robot Recomposition in the Field with Unseen Software, Hardware, and Compute Payloads." pith.science (2026). https://pith.science/paper/YPO3MHU7

@misc{pith2026260811063,
  author       = {Pith},
  title        = {Pith review of: Deployment Is Not Destiny: Robot Recomposition in the Field with Unseen Software, Hardware, and Compute Payloads},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YPO3MHU7}},
  note         = {Machine review of arXiv:2608.11063}
}
read the original abstract

The tight coupling of subsystems in most robots, though a natural consequence of their complexity, leads to monolithic designs that are time-consuming and difficult to adapt after initial deployment. To address this challenge, we present a framework and supporting abstractions for recomposition during runtime that enable robots to quickly integrate previously unseen modular software, hardware, and compute payloads. Our approach allows non-expert users to quickly add new capabilities in the field through a true plug-and-play process. Crucially, new resources are not only immediately available to a host robot but are also shared with distributed peers, enabling compute-constrained systems to access powerful new remote capabilities. Our framework reduces reconfiguration time to a matter of minutes with no developer intervention, in stark contrast to the hours of expert effort often required for traditional manual integration. We demonstrate our method in two disaster response scenarios, including radioactive source localization at an operational nuclear reactor facility and a thermal-guided search for people in dark, difficult-to-reach spaces. These demonstrations show how in-field recomposition provides timely, flexible, and accessible adaptation to dynamic requirements, representing a critical step toward creating robots that can quickly evolve alongside the tasks, technologies, and environments they support.

Figures

Figures reproduced from arXiv: 2608.11063 by the authors.

Figure 1
Figure 1. Core Concept: Our framework allows robots to be quickly modified with plug-and-play nondum visum payloads for ad hoc tasks. Non￾roboticist end-users can quickly reconfigure a robot in the field without reprogramming, and robots require no prior knowledge of introduced payloads. Within minutes, a robot can compose new capabilities into goal￾directed task plans. new technologies become available, and reconfiguration f… view at source ↗
Figure 2
Figure 2. Framework Overview: The system begins with a set of software, hardware, and compute initial components. Each software component affords a set of behaviors that are composed into a system-level behavior tree responsible for processing system updates and task-planning. A dedicated component manager is responsible for discovery and management of nondum visum added components introduced during runtime. When provided a t… view at source ↗
Figure 3
Figure 3. Host Robots and Nondum Visum Payloads: Each host robot and the external compute payload starts with a subset of initial software components from [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Demonstration 1: Radioactive Source Localization consisting of stage I) person-following traversal through the building using an RGB-D camera, stage II) continued traversal through a dark part of the facility using a thermal camera, and stage III) radiation source loca…
Figure 5
Figure 5. Figure 5: Demonstration 2: Thermal-Guided Person Search consisting of stage I) an outdoor survey with a Panther robot and stage II) the transition to a Turtlebot robot capable of entering a dark building through a small opening. Both robots are initially computationally limited …
Figure 6
Figure 6. Figure 6: Demonstration Outcomes: (a) Venn diagram showing the reuse of hardware, software, and compute payloads across host robots. For robots that deployed the external compute payload ( 2 , 3 ) the payloads deployed on the external compute are shaded gray. (b) Validation of p…

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Reference graph

Works this paper leans on

39 extracted references · 36 canonical work pages

  1. [14]

    CORAL: A unifying abstraction layer for compositional robotics software,

    S. Swanbeck and M. Pryor, “CORAL: A unifying abstraction layer for compositional robotics software,” inProc. IEEE/SICE Int. Symp. Syst. Integration, 2026

    work page 2026

  2. [1]

    A framework for rapid robotic application development for citizen developers,

    K. Panayiotou, E. Tsardoulias, C. Zolotas, A. L. Symeonidis, and L. Petrou, “A framework for rapid robotic application development for citizen developers,”Software, vol. 1, no. 1, 2022

    work page 2022

  3. [2]

    Component-based robotic engineering (part I),

    D. Brugali and P. Scandurra, “Component-based robotic engineering (part I),”IEEE Robot. Automat. Mag., vol. 16, no. 4, 2009

    work page 2009

  4. [3]

    Software reconfiguration in robotics,

    S. Peldszus, D. Brugali, D. Str ¨uber, P. Pelliccione, and T. Berger, “Software reconfiguration in robotics,”Empirical Softw. Eng., vol. 30, no. 3, 2025

    work page 2025

  5. [4]

    A methodical interpretation of adaptive robotics: Study and reformulation,

    A. M. S. Enayati, Z. Zhang, and H. Najjaran, “A methodical interpretation of adaptive robotics: Study and reformulation,”Neu- rocomputing, vol. 512, 2022

    work page 2022

  6. [5]

    Toward general-purpose robots via foundation models: A survey and meta-analysis,

    Y . Hu et al., “Toward general-purpose robots via foundation models: A survey and meta-analysis,”arXiv preprint arXiv:2312.08782, 2023

    arXiv 2023

  7. [6]

    Current trends in reconfigurable modular robots design,

    A. Brunete, A. Ranganath, S. Segovia, J. P. De Frutos, M. Hernando, and E. Gambao, “Current trends in reconfigurable modular robots design,”Int. J. Adv. Robot. Syst., vol. 14, no. 3, 2017

    work page 2017

  8. [7]

    A plug and produce framework for industrial collaborative robots,

    C. Schou and O. Madsen, “A plug and produce framework for industrial collaborative robots,”Int. J. Adv. Robot. Syst., vol. 14, no. 4, 2017

    work page 2017

Show all 39 references
  1. [8]

    Concert: A modular reconfigurable robot for construction,

    L. Rossini et al., “Concert: A modular reconfigurable robot for construction,”J. Field Robot., vol. 43, no. 3, pp. 1332–1362, 2026

    2026

  2. [9]

    Design of dynam- ically reconfigurable real-time software using port-based objects,

    D. B. Stewart, R. A. V olpe, and P. K. Khosla, “Design of dynam- ically reconfigurable real-time software using port-based objects,” IEEE Trans. Softw. Eng., vol. 23, no. 12, 1997

    1997

  3. [10]

    An extendable frame- work for intelligent and easily configurable skills-based industrial robot applications,

    L. Heuss, C. Gonnermann, and G. Reinhart, “An extendable frame- work for intelligent and easily configurable skills-based industrial robot applications,”Int. J. Adv. Manuf. Technol., vol. 120, no. 9, 2022

    2022

  4. [11]

    Robot Operating System 2: Design, architecture, and uses in the wild,

    S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot Operating System 2: Design, architecture, and uses in the wild,”Sci. Robot., vol. 7, no. 66, 2022

    2022

  5. [12]

    The XBot2 real-time middleware for robotics,

    A. Laurenzi, D. Antonucci, N. G. Tsagarakis, and L. Muratore, “The XBot2 real-time middleware for robotics,”Robot. Auton. Syst., vol. 163, 2023

    2023

  6. [13]

    µRT: A lightweight real-time middleware with integrated validation of timing constraints,

    T. Sch ¨opping, S. Kenneweg, M. Hesse, and U. R ¨uckert, “µRT: A lightweight real-time middleware with integrated validation of timing constraints,”Frontiers Robot. AI, vol. 10, 2023

    2023

  7. [15]

    Scheduling dynamic software updates in mobile robots,

    A. E. Yaacoub, L. Mottola, T. V oigt, and P. R ¨ummer, “Scheduling dynamic software updates in mobile robots,”ACM Trans. Embedded Comput. Syst., vol. 22, no. 6, 2023

    2023

  8. [16]

    Software variability in service robotics,

    S. Garc ´ıa, D. Str ¨uber, D. Brugali, A. Di Fava, P. Pelliccione, and T. Berger, “Software variability in service robotics,”Emp. Softw. Eng., vol. 28, no. 2, 2023

    2023

  9. [17]

    TeMoto: A software framework for adaptive and dependable robotic autonomy with dynamic resource management,

    R. Valner, V . Vunder, A. Aabloo, M. Pryor, and K. Kruusam ¨ae, “TeMoto: A software framework for adaptive and dependable robotic autonomy with dynamic resource management,”IEEE Ac- cess, vol. 10, 2022

    2022

  10. [18]

    The pluggable distributed resource allocator (PDRA): A middleware for distributed computing in mobile robotic networks,

    F. Rossi, T. S. Vaquero, M. Sanchez-Net, M. S. da Silva, and J. Vander Hook, “The pluggable distributed resource allocator (PDRA): A middleware for distributed computing in mobile robotic networks,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2020

    2020

  11. [19]

    Resource allocation and service provisioning in multi-agent cloud robotics: A comprehensive survey,

    M. Afrin, J. Jin, A. Rahman, A. Rahman, J. Wan, and E. Hossain, “Resource allocation and service provisioning in multi-agent cloud robotics: A comprehensive survey,”IEEE Commun. Surv. & Tut., vol. 23, no. 2, 2021

    2021

  12. [20]

    FogROS2: An adaptive platform for cloud and fog robotics using ROS 2,

    J. Ichnowski et al., “FogROS2: An adaptive platform for cloud and fog robotics using ROS 2,” inProc. IEEE Int. Conf. Robot. Automat., 2023

    2023

  13. [21]

    Modular self-reconfigurable robotic systems: A survey on hardware archi- tectures,

    S. S. R. Chennareddy, A. Agrawal, and A. Karuppiah, “Modular self-reconfigurable robotic systems: A survey on hardware archi- tectures,”J. Robot., 2017

    2017

  14. [22]

    Decoding modular reconfigurable robots: A survey on mechanisms and design,

    G. Liang, D. Wu, Y . Tu, and T. L. Lam, “Decoding modular reconfigurable robots: A survey on mechanisms and design,”Int. J. Robot. Res., vol. 44, no. 5, 2024

    2024

  15. [23]

    The Thorvald II agricultural robotic system,

    L. Grimstad and P. From, “The Thorvald II agricultural robotic system,”Robotics, vol. 6, no. 4, 2017

    2017

  16. [24]

    A modular agricultural robotic system (MARS) for precision farming: Concept and implementation,

    R. Xu and C. Li, “A modular agricultural robotic system (MARS) for precision farming: Concept and implementation,”J. Field Robot., vol. 39, no. 4, 2022

    2022

  17. [25]

    Hefty: A modular re- configurable robot for advancing robot manipulation in agriculture,

    D. Guri, M. Lee, O. Kroemer, and G. Kantor, “Hefty: A modular re- configurable robot for advancing robot manipulation in agriculture,” arXiv preprint: arXiv:2402.18710, 2024

    2024 arXiv

  18. [26]

    Enhancing development of modular application-specific configurable space robots,

    H. Wiedemann et al., “Enhancing development of modular application-specific configurable space robots,”J. Phys.: Conf. Ser., vol. 2716, no. 1, 2024

    2024

  19. [27]

    Modular and reconfigurable mobile robotics,

    P. Moubarak and P. Ben-Tzvi, “Modular and reconfigurable mobile robotics,”Robot. Auton. Syst., vol. 60, no. 12, 2012

    2012

  20. [28]

    Agile assembly system by “plug and produce

    T. Arai, Y . Aiyama, Y . Maeda, M. Sugi, and J. Ota, “Agile assembly system by “plug and produce”,”CIRP Annals - Manuf. Technol., vol. 49, no. 1, 2000

    2000

  21. [29]

    A generic plug & produce system composed of semantic OPC UA skills,

    S. Profanter, A. Perzylo, M. Rickert, and A. Knoll, “A generic plug & produce system composed of semantic OPC UA skills,”IEEE Open J. Ind. Electron. Soc., vol. 2, 2021

    2021

  22. [30]

    Modular robot software framework for the intelligent and flexible composition of its skills,

    L. Heuss, A. Blank, S. Dengler, G. L. Zikeli, G. Reinhart, and J. Franke, “Modular robot software framework for the intelligent and flexible composition of its skills,” inIFIP Int. Conf. Adv. Prod. Manage. Syst., 2019

    2019

  23. [31]

    Censi, J

    A. Censi, J. Lorand, and G. Zardini,Applied Compositional Think- ing for Engineers. 2022, Work in Progress Book

    2022

  24. [32]

    Docker: Lightweight Linux containers for consistent development and deployment,

    D. Merkel, “Docker: Lightweight Linux containers for consistent development and deployment,”Linux J., vol. 239, no. 2, 2014

    2014

  25. [33]

    How behavior trees modularize hybrid control systems and generalize sequential behavior composi- tions, the subsumption architecture, and decision trees,

    M. Colledanchise and P. ¨Ogren, “How behavior trees modularize hybrid control systems and generalize sequential behavior composi- tions, the subsumption architecture, and decision trees,”IEEE Trans. Robot., vol. 33, no. 2, 2016

    2016

  26. [34]

    PDDL-the planning domain definition language,

    M. Ghallab et al., “PDDL-the planning domain definition language,” Yale Center for Computational Vision and Control, Tech. Rep. CVC TR-98-003/DCS TR-1165, 1998

    1998

  27. [35]

    Robust speech recognition via large-scale weak supervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” inInt. Conf. Mach. Learn., 2023

    2023

  28. [36]

    The Llama 3 herd of models,

    A. Grattafiori et al., “The Llama 3 herd of models,”arXiv preprint arXiv:2407.21783, 2024

    2024 arXiv

  29. [37]

    Plan- Sys2: A planning system framework for ROS2,

    F. Mart ´ın, J. G. Clavero, V . Matell ´an, and F. J. Rodr ´ıguez, “Plan- Sys2: A planning system framework for ROS2,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2021

    2021

  30. [38]

    Depth Pro: Sharp monocular metric depth in less than a second,

    A. Bochkovskii et al., “Depth Pro: Sharp monocular metric depth in less than a second,”arXiv preprint arXiv:2410.02073, 2024

    2024 arXiv

  31. [39]

    A study on challenges of testing robotic systems,

    A. Afzal, C. Le Goues, M. Hilton, and C. S. Timperley, “A study on challenges of testing robotic systems,” inProc. IEEE Int. Conf. Softw. Testing, Validation and Verification, 2020

    2020

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