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REVIEW 3 major objections 5 minor 45 references

TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A robot-and-drone team aims to stream live 3D semantic maps to first responders.

desk verdict Coherent EU-project system overview for a UAV/UGV first-responder platform, but it contains zero experimental results and rests on an unvalidated private wireless network assumption. read the letter →

arxiv 2502.09379 v2 pith:DJ4MQVJD submitted 2025-02-13 cs.RO cs.AI

Jorgen Cani , Panagiotis Koletsis , Konstantinos Foteinos , Ioannis Kefaloukos , Lampros Argyriou , Manolis Falelakis , Iván Del Pino , Angel Santamaria-Navarro
show 9 more authors
Martin Čech Ondřej Severa Alessandro Umbrico Francesca Fracasso AndreA Orlandini Dimitrios Drakoulis Evangelos Markakis Iraklis Varlamis Georgios Th. Papadopoulos
This is my paper · ORCID
classification cs.ROcs.AI
keywords roboticdisasterresponseunmannedgroundvehicleaerialsemanticmappingsituationalawarenessfirstrespondersaugmentedrealitymissionplanning
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 a unified robotic system—a ground robot and a drone working with a ground station, field-crew smartphones, and a private wireless network—can give first responders an edge in wildfires, urban floods, and post-earthquake search and rescue. The central claim is that this combination delivers real-time semantic 3D mapping, autonomous navigation, and augmented-reality interfaces that strengthen mission planning, safety monitoring, and adaptive task execution. A sympathetic reading takes TRIFFID as an architectural blueprint: the paper specifies modules for navigation, perception, human-robot interaction, and communication rather than reporting measured field performance. If the design works as described, response teams would get near-real-time situational awareness while keeping a human operator in the loop.

What carries the argument

The load-bearing mechanism is a closed control loop that runs from the robots' sensors to the ground station, where LiDAR- and RGB-based maps are merged with Kalman filtering into a semantically annotated 3D area map, enriched by a knowledge graph and broadcast to crew smartphones and the AR operator interface. From that map, a three-layer mission and task planner—mission planning, global motion planning, and local traversability navigation—generates robot behavior through behavior trees, with a sliding-window nonlinear model predictive controller for dynamic path planning and obstacle avoidance. The custom private wireless network (4G, Wi-Fi, possibly 5G) is the enabling infrastructure that keeps this loop real-time and supports the described fail-safe behaviors, including return-to-base and rejoin-crew maneuvers during communication failures.

What would settle it

Run a field exercise at a realistic disaster site with the UGV, UAV, ground station, and crew smartphones connected through the system's private 4G/Wi-Fi network, then measure the time from a UGV sensor capture to a visible update on a crew smartphone. If end-to-end latency exceeds what the fail-safe logic assumes, or if interrupting the network prevents the UGV from executing its return-to-base fail-safe, the paper's central operational claim is not supported.

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Extended reading notes

Core claim

The paper's central assertion is that an integrated robotics-and-AI system, built from a hybrid UGV/UAV pair, a centralized ground station with an AR interface, a custom low-latency private wireless network, and a smartphone app for each crew member, can enhance first-responder operations. It claims the system will provide real-time semantic mapping, autonomous navigation, and augmented-reality interfaces, with robots able to conduct remote reconnaissance and deliver critical information while a human operator retains oversight. The design covers three demanding scenarios: a wildfire nearing an industrial facility, an urban flood with a chemical-plant hazard, and urban search and rescue after an earthquake. The paper's contribution is the coherent combination of these technologies into one operational workflow, not an experimental demonstration of that workflow.

Load-bearing premise

The entire workflow depends on deploying and maintaining a private low-latency wireless network that covers the disaster site; if that link drops or lags, real-time map updates, teleoperation, and the fail-safe behaviors described in the paper cannot operate.

Editorial extensions

If this is right

  • First responders would see a continuously updated, semantically labeled 3D map of the disaster area on their smartphones, letting them spot hazards and survivors faster.
  • The UGV could handle autonomous tasks such as mapping, delivering supplies, searching for civilians, and checking for hazards, removing crew members from dangerous positions.
  • Mission plans would adapt in real time when goals change or new obstacles appear, with the option to redeploy the UAV for a targeted aerial survey.
  • A remote backup pilot and manual teleoperation would keep a human in the loop, so the system can work even when autonomous navigation fails.
  • The modular architecture is designed to be reconfigurable, meaning the same platform could be adapted to other disaster types such as industrial or CBRN incidents.

Reading between the lines

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

  • A testable consequence the paper leaves implicit is how system performance degrades when the private wireless link is intermittent; measuring end-to-end latency and map-update rate under partial connectivity would bound the real-time claim.
  • Because the design uses a fine-tuned LLM for verbal commands, a likely failure mode is misinterpretation of natural-language instructions in noisy field conditions, which could be probed through adversarial command tests with first responders.
  • If the semantic map fusion is as good as claimed, the same ground-station backend could be reused for non-robotic data sources, such as fixed sensors or public CCTV, extending situational awareness beyond the robots' own sensors.
  • The emphasis on public outreach and training suggests the authors implicitly assume organizational adoption is a bottleneck; a useful extension would be a cost-benefit model comparing TRIFFID against current drone-only or UGV-only deployments.
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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 TRIFFID, a proposed disaster-response robot system that integrates an unmanned ground vehicle (UGV), an unmanned aerial vehicle (UAV), a ground station with augmented reality, a private wireless communication infrastructure, and a smartphone app for first responders. The system is designed for three use cases: suburban wildfire, urban flood, and post-earthquake search and rescue. The manuscript describes the overall architecture, individual modules for mission/task planning, autonomous navigation, safety monitoring, communications, semantic perception, human-robot interaction, and AR visualization, all expressed as planned or future developments. No experiments, simulations, or field results are reported; the paper is a system design and project overview.

Significance. If realized and validated, TRIFFID would address a relevant problem: improving situational awareness and reducing personnel risk in disaster response. The modular architecture is coherent, the three use cases are realistic and important, and the paper clearly situates the proposal within prior work and international projects such as SILVANUS, CARMA, and CURSOR. It also builds on a substantial body of prior publications by the same authors, which lends credibility to the individual component techniques. However, as a standalone journal contribution, the paper offers no evidence that the proposed system can meet its performance claims. The central assertion that TRIFFID 'enhances emergency response teams' is an unverified prediction, not a demonstrated result. The paper is therefore best viewed as a research proposal or project deliverable rather than a completed scientific study.

major comments (3)
  1. [Sections IV and VI] The manuscript contains no experimental, simulation, or field evaluation. Every module is described in future tense ('will implement', 'will be developed', 'will be assessed'), and Section VI explicitly defers 'extensive field testing' to future work. The abstract's claim that 'The proposed system enhances emergency response teams by providing advanced mission planning, safety monitoring, and adaptive task execution capabilities' is therefore unsupported. This is a load-bearing gap: the central contribution of the paper is an asserted benefit rather than a demonstrated one. The authors should either add a validation section with quantitative results (e.g., simulation of the mission-planning or perception modules, or a small-scale field trial with the UGV/UAV) or explicitly revise the abstract and conclusion to state that the paper presents a design specification and research agenda, not an evaluated system.
  2. [Section III, and Section IV-A4] The entire TRIFFID workflow depends on a private, low-latency wireless network connecting the UGV, UAV, ground station, and FR smartphones, with hotspots 'strategically deployed' across the disaster area. In the three use cases, the environment is explicitly hostile to such a network: wildfire winds over 70 km/h, flood-compromised infrastructure, and earthquake rubble. The paper gives no link budget, coverage analysis, node-count estimate, power-source plan, or latency requirements, and the fallback clause 'switch to alternative options' is undefined. If the network degrades or drops, the near-real-time map updates, teleoperation, and the debris-triggered UAV survey workflow (Section III) cannot operate as described. Please provide a quantitative communication analysis or at least a clear statement of the minimum connectivity required for each subsystem, along with a degradation-handling protocol with specific behavior under partial or temporary loss of connectivity.
  3. [Section IV-B1 and IV-B2] The near-real-time semantic 3D map pipeline is central to the system's value proposition, but no computational budget or latency estimate is given. The pipeline includes LiDAR-based SLAM, RGB-based semantic segmentation, diffusion-model point cloud completion, Kalman-filter fusion, and KG enrichment, culminating in AR display. No analysis shows how these stages meet the timing implied by 'near-real-time' on the described robot and ground-station hardware. Please include a latency/throughput analysis per stage, or specify update-rate requirements and the expected total map-generation delay, so that the claim of 'real-time situational awareness' can be assessed.
minor comments (5)
  1. [Section IV-B1] Reference [26] is cited for the use of diffusion models for completing missing areas in 3D point clouds, but [26] is a review of diffusion models for image data augmentation. Please clarify the connection or replace the reference with one directly applicable to point cloud completion.
  2. [Figures 1 and 2] The architecture figures are reproduced but not described in the text. A brief paragraph summarizing the main blocks and information flow in each figure would help readers, especially since the figures are likely to be viewed independently.
  3. [Section IV-A2] There is a typo, 'UVG' should be 'UGV' in the sentence about safe UGV navigation in harsh environments. Throughout the paper, 'UA Vs' and 'UAV's' are used inconsistently; please standardize.
  4. [Section V] The use cases are described with future-tense language such as 'will be demonstrated', but the paper does not demonstrate them. Consider renaming this section 'Use-Case Scenarios' and phrasing the descriptions as intended operational contexts, not as results.
  5. [Section IV-B3] The claim that a novel ontology extends the IEEE RAS ontology is made but no specifics are given. A short description of the new concepts (e.g., health state, needs of civilians, physiological state of FRs) and their relationships would strengthen the design description.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: TRIFFID is an architecture proposal with no fitted parameters or derivation chain; self-citations are non-load-bearing.

full rationale

This manuscript is a system and architecture description for the TRIFFID project. It contains no numerical experiments, no fitted parameters, no equations, and no derivation chain in which an output quantity is constructed from an input quantity. The core claims, such as 'TRIFFID will provide FRs with real-time semantic mapping, autonomous navigation, and augmented reality interfaces,' are forward-looking design goals, not predictions derived from data or from the cited works. The many citations to prior work by the same authors (e.g., [14], [16], [17], [19], [22], [24], [28], [33], [34], [36], [39]) are used as methodological starting points or related techniques to be extended; none is invoked as a uniqueness theorem, none forbids alternative implementations, and the paper's central value proposition does not collapse if any single citation were unsound. The skeptical point about the private low-latency wireless network is a feasibility and robustness assumption, not a circular reduction: the described system behavior is conditional on the link, but the claim 'TRIFFID will enhance emergency response' is not defined in terms of the network's success. No circular step can be quoted and exhibited, so the score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims rest on several domain assumptions about disaster-site conditions that the paper does not validate: network availability, perception robustness, and terrain traversability. No free numerical parameters are fitted because the paper contains no experiments. No new physical or conceptual entities are introduced beyond the planned system components themselves.

assumptions (3)
  • domain assumption A private low-latency wireless network covering the disaster site can be deployed and maintained.
    Stated in Section IV-A4 and Section III; no evidence that hotspots will function on damaged infrastructure.
  • domain assumption Deep learning perception models will work reliably in disaster conditions such as smoke, dust, fire, and low visibility.
    Stated in Sections IV-B1 and IV-B2; the paper only lists planned techniques to address scarcity.
  • domain assumption Off-the-shelf or lightly customized navigation components can handle harsh unstructured terrain and debris.
    Stated in Section IV-A2; the paper notes UGV navigation is the most challenging implementation.

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Cite this review

Pith. "Pith review of TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency." pith.science (2026). https://pith.science/paper/DJ4MQVJD

@misc{pith2026250209379,
  author       = {Pith},
  title        = {Pith review of: TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DJ4MQVJD}},
  note         = {Machine review of arXiv:2502.09379}
}
read the original abstract

The increasing complexity of natural disaster incidents demands innovative technological solutions to support first responders in their efforts. This paper introduces the TRIFFID system, a comprehensive technical framework that integrates unmanned ground and aerial vehicles with advanced artificial intelligence functionalities to enhance disaster response capabilities across wildfires, urban floods, and post-earthquake search and rescue missions. By leveraging state-of-the-art autonomous navigation, semantic perception, and human-robot interaction technologies, TRIFFID provides a sophisticated system composed of the following key components: hybrid robotic platform, centralized ground station, custom communication infrastructure, and smartphone application. The defined research and development activities demonstrate how deep neural networks, knowledge graphs, and multimodal information fusion can enable robots to autonomously navigate and analyze disaster environments, reducing personnel risks and accelerating response times. The proposed system enhances emergency response teams by providing advanced mission planning, safety monitoring, and adaptive task execution capabilities. Moreover, it ensures real-time situational awareness and operational support in complex and risky situations, facilitating rapid and precise information collection and coordinated actions.

Figures

Figures reproduced from arXiv: 2502.09379 by the authors.

Figure 1
Figure 1. TRIFFID system functional architecture. a UAV and UGV for autonomous reconnaissance in three demanding real-world scenarios, namely wildfire, urban flood, and earthquake. Table I summarizes key research and devel￾opment efforts for deploying robotic solutions in post-disaster scenarios. III. PROPOSED SYSTEM The ’Autonomous robotic aid for increasing first responders efficiency’ system (entitled TRIFFID), whose overa… view at source ↗
Figure 2
Figure 2. TRIFFID system technical architecture. survey. The re-deployed UAV autonomously navigates to the specified area, surveys it, and sends data back to the ground￾station, updating the map automatically. Eventually, the UGV receives the new map wirelessly and proceeds with its task if possible; otherwise, it executes a suitable fail-safe behavior (e.g., return to BoO, rejoin the FR crew, request new orders, etc.). This … view at source ↗
Figure 3
Figure 3. Indicative visual content from TRIFFID’s supported use cases: i) Wildfire in suburban environments [42], ii) Urban [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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Works this paper leans on

45 extracted references · 45 canonical work pages

  1. [1]

    Publications Office of the European Union, 2021, ISBN : 978-92-76-29303-3

    Directorate-General for European Civil Protection and Hu- manitarian Aid Operations (ECHO) (European Commission), Overview of Natural and Man-Made Disaster Risks the Eu- ropean Union May Face: 2020 Edition . Publications Office of the European Union, 2021, ISBN : 978-92-76-29303-3. [Online]. Available: https://data.europa.eu/doi/10.2795/1521

    work page doi:10.2795/1521 2020

  2. [2]

    The State of European Cities 2016: Cities leading the way to a better future — UN-Habitat

    “The State of European Cities 2016: Cities leading the way to a better future — UN-Habitat.” (2016), [Online]. Available: https://unhabitat.org/the-state-of-european-cities-2016-cities- leading-the-way-to-a-better-future

    work page 2016

  3. [3]

    A Learning-Based Semi- Autonomous Controller for Robotic Exploration of Unknown Disaster Scenes While Searching for Victims,

    B. Doroodgar, Y . Liu, and G. Nejat, “A Learning-Based Semi- Autonomous Controller for Robotic Exploration of Unknown Disaster Scenes While Searching for Victims,” IEEE Trans- actions on Cybernetics , vol. 44, no. 12, pp. 2719–2732, Dec. 2014, ISSN : 2168-2275

    work page 2014

  4. [4]

    Post-disaster assessment with unmanned aerial vehicles: A survey on practical im- plementations and research approaches,

    C. T. Recchiuto and A. Sgorbissa, “Post-disaster assessment with unmanned aerial vehicles: A survey on practical im- plementations and research approaches,” Journal of Field Robotics, vol. 35, no. 4, pp. 459–490, 2018, ISSN : 1556-4967

    work page 2018

  5. [5]

    Detection of Damaged Infrastructure on Disaster Sites using Mobile Robots,

    J. Chen and Y . K. Cho, “Detection of Damaged Infrastructure on Disaster Sites using Mobile Robots,” in 2019 16th Inter- national Conference on Ubiquitous Robots (UR) , Jun. 2019, pp. 648–653

    work page 2019

  6. [6]

    Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints,

    P. Ghassemi and S. Chowdhury, “Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints,” Robotics and Autonomous Systems, vol. 147, p. 103 905, Jan. 1, 2022, ISSN : 0921-8890

    work page 2022

  7. [7]

    Search and Reconnaissance Robot for Disaster Management,

    S. Narayan, M. Aquif, A. R. Kalim, D. Chagarlamudi, and M. Harshith Vignesh, “Search and Reconnaissance Robot for Disaster Management,” in Machines, Mechanism and Robotics, R. Kumar, V . S. Chauhan, M. Talha, and H. Pathak, Eds., Singapore: Springer, 2022, pp. 187–201, ISBN : 978-981- 16-0550-5

    work page 2022

  8. [8]

    Survivor detection ap- proach for post earthquake search and rescue missions based on deep learning inspired algorithms,

    R. Jadeja, T. Trivedi, and J. Surve, “Survivor detection ap- proach for post earthquake search and rescue missions based on deep learning inspired algorithms,” Sci Rep, vol. 14, no. 1, p. 25 047, Oct. 23, 2024, ISSN : 2045-2322

    work page 2024

Show all 45 references
  1. [9]

    A hetero- geneous robots collaboration for safety, security, and rescue robotics: E-ASIA joint research program for disaster risk and reduction management,

    B. M. Pillai, J. Suthakorn, D. Sivaraman, et al. , “A hetero- geneous robots collaboration for safety, security, and rescue robotics: E-ASIA joint research program for disaster risk and reduction management,” Advanced Robotics , vol. 38, no. 3, pp. 129–151, Feb. 1, 2024, ISSN ...

    2024

  2. [10]

    Autonomous post-disaster indoor navigation and survivor detection using low-cost micro aerial vehicles,

    S. Tavasoli, S. Poorghasem, X. Pan, T. Y . Yang, and Y . Bao, “Autonomous post-disaster indoor navigation and survivor detection using low-cost micro aerial vehicles,” Computer- Aided Civil and Infrastructure Engineering , vol. 40, no. 1, pp. 130–144, 2025, ISSN : 1467-8667

    2025

  3. [11]

    CARMA Project,

    “CARMA Project,” CARMA. (2024), [Online]. Available: https://www.carmarobots.eu/carma-project/

    2024

  4. [12]

    SILV ANUS — Integrated Technological and Information Platform for Wildfire Management,

    “SILV ANUS — Integrated Technological and Information Platform for Wildfire Management,” SILV ANUS. (2021), [On- line]. Available: https://silvanus-project.eu/

    2021

  5. [13]

    The CURSOR Search and Rescue (SaR) Kit: An innovative solution for improving the efficiency of Urban SaR Operations,

    T. Ristmae, D. Dionysiou, M. Koutsokeras, A. Douklias, E. Ouzounoglou, and A. Amditis, “The CURSOR Search and Rescue (SaR) Kit: An innovative solution for improving the efficiency of Urban SaR Operations,” 2021

    2021

  6. [14]

    Terrain Classification in Complex Three- dimensional Outdoor Environments,

    `A. Santamaria-Navarro, E. H. Teniente, M. Morta, and J. Andrade-Cetto, “Terrain Classification in Complex Three- dimensional Outdoor Environments,” Journal of Field Robotics, vol. 32, no. 1, pp. 42–60, 2015, ISSN : 1556-4967

    2015

  7. [15]

    A survey of Behavior Trees in robotics and AI,

    M. Iovino, E. Scukins, J. Styrud, P. ¨Ogren, and C. Smith, “A survey of Behavior Trees in robotics and AI,” Robotics and Autonomous Systems, vol. 154, p. 104 096, Aug. 1, 2022, ISSN : 0921-8890

    2022

  8. [16]

    To- ward the Deployment of an Autonomous Last-Mile Delivery Robot in Urban Areas: The Ona Prototype Platform,

    A. Santamaria-Navarro, S. Hern ´andez, F. Herrero, et al., “To- ward the Deployment of an Autonomous Last-Mile Delivery Robot in Urban Areas: The Ona Prototype Platform,” IEEE Robotics & Automation Magazine , pp. 2–15, 2024, ISSN : 1558-223X

    2024

  9. [17]

    Dis- tributed Maze Exploration Using Multiple Agents and Opti- mal Goal Assignment,

    M. Linardakis, I. Varlamis, and G. T. Papadopoulos, “Dis- tributed Maze Exploration Using Multiple Agents and Opti- mal Goal Assignment,” IEEE Access , vol. 12, pp. 101 407– 101 418, 2024, ISSN : 2169-3536

    2024

  10. [18]

    NeBula: TEAM CoSTAR’s Robotic Autonomy Solution that Won Phase II of DARPA Subterranean Challenge,

    A. Agha, K. Otsu, B. Morrell, et al. , “NeBula: TEAM CoSTAR’s Robotic Autonomy Solution that Won Phase II of DARPA Subterranean Challenge,” Field Robotics, vol. 2, no. 1, pp. 1432–1506, 1 2022, ISSN : 2771-3989

    2022

  11. [19]

    Probabilistic Graph-Based Real- Time Ground Segmentation for Urban Robotics,

    I. del Pino, A. Santamaria-Navarro, A. Garrell Zulueta, F. Tor- res, and J. Andrade-Cetto, “Probabilistic Graph-Based Real- Time Ground Segmentation for Urban Robotics,” IEEE Trans- actions on Intelligent Vehicles , vol. 9, no. 5, pp. 4989–5002, May 2024, ISSN : 2379-8904

    2024

  12. [20]

    Dynamic Tube MPC for Nonlinear Systems,

    B. T. Lopez, J.-J. E. Slotine, and J. P. How, “Dynamic Tube MPC for Nonlinear Systems,” in 2019 American Control Conference (ACC), Jul. 2019, pp. 1655–1662

    2019

  13. [21]

    The Marathon 2: A Navigation System,

    S. Macenski, F. Mart ´ın, R. White, and J. G. Clavero, “The Marathon 2: A Navigation System,” in 2020 IEEE/RSJ In- ternational Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 2718–2725

    2020

  14. [22]

    Towards Resilient Autonomous Navigation of Drones,

    A. Santamaria-Navarro, R. Thakker, D. D. Fan, B. Morrell, and A.-a. Agha-mohammadi, “Towards Resilient Autonomous Navigation of Drones,” in Robotics Research, T. Asfour, E. Yoshida, J. Park, H. Christensen, and O. Khatib, Eds., Cham: Springer International Publishing, 2022, pp...

    2022

  15. [23]

    On the Weighted Cluster S-UA V Scheme Using Latency-Oriented Trust,

    G. Khayat, C. X. Mavromoustakis, A. Pitsillides, J. M. Batalla, E. K. Markakis, and G. Mastorakis, “On the Weighted Cluster S-UA V Scheme Using Latency-Oriented Trust,”IEEE Access, vol. 11, pp. 56 310–56 323, 2023, ISSN : 2169-3536

    2023

  16. [24]

    Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions,

    N. Rodis, C. Sardianos, P. Radoglou-Grammatikis, P. Sari- giannidis, I. Varlamis, and G. T. Papadopoulos, “Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions,” IEEE Access, 2024

    2024

  17. [25]

    Regularized Deep Signed Distance Fields for Reactive Motion Generation,

    P. Liu, K. Zhang, D. Tateo, S. Jauhri, J. Peters, and G. Chalvatzaki, “Regularized Deep Signed Distance Fields for Reactive Motion Generation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2022, pp. 6673–6680

    2022

  18. [26]

    Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions,

    P. Alimisis, I. Mademlis, P. Radoglou-Grammatikis, P. Sari- giannidis, and G. T. Papadopoulos, “Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions,” Artif Intell Rev , vol. 58, no. 4, p. 112,...

    2025

  19. [27]

    WOLF: A Modular Estima- tion Framework for Robotics Based on Factor Graphs,

    J. Sol `a, J. Vallv´e, J. Casals, et al., “WOLF: A Modular Estima- tion Framework for Robotics Based on Factor Graphs,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4710–4717, Apr. 2022, ISSN : 2377-3766

    2022

  20. [28]

    Self-supervised visual learning in the low-data regime: A comparative evalu- ation,

    S. Konstantakos, J. Cani, I. Mademlis, et al., “Self-supervised visual learning in the low-data regime: A comparative evalu- ation,” Neurocomputing, vol. 620, p. 129 199, Mar. 1, 2025, ISSN : 0925-2312

    2025

  21. [29]

    A Compre- hensive Study on Deep Learning-Based Methods for Sign Language Recognition,

    N. Adaloglou, T. Chatzis, I. Papastratis, et al. , “A Compre- hensive Study on Deep Learning-Based Methods for Sign Language Recognition,” IEEE Transactions on Multimedia , vol. 24, pp. 1750–1762, 2022, ISSN : 1941-0077

    2022

  22. [30]

    Intention-based Long- Term Human Motion Anticipation,

    J. Tanke, C. Zaveri, and J. Gall, “Intention-based Long- Term Human Motion Anticipation,” in 2021 International Conference on 3D Vision (3DV) , Dec. 2021, pp. 596–605

    2021

  23. [31]

    Du- alFormer: Local-Global Stratified Transformer for Efficient Video Recognition,

    Y . Liang, P. Zhou, R. Zimmermann, and S. Yan, “Du- alFormer: Local-Global Stratified Transformer for Efficient Video Recognition,” in Computer Vision – ECCV 2022 , S. Avidan, G. Brostow, M. Ciss ´e, G. M. Farinella, and T. Hassner, Eds., Cham: Springer Nature Switzerland, 202...

    2022

  24. [32]

    Multi-Task Learning for Video Surveillance with Limited Data,

    K. Doshi and Y . Yilmaz, “Multi-Task Learning for Video Surveillance with Limited Data,” in 2022 IEEE/CVF Con- ference on Computer Vision and Pattern Recognition Work- shops (CVPRW), New Orleans, LA, USA: IEEE, Jun. 2022, pp. 3888–3898, ISBN : 978-1-6654-8739-9

    2022

  25. [33]

    User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative envi- ronments,

    G. T. Papadopoulos, A. Leonidis, M. Antona, and C. Stephani- dis, “User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative envi- ronments,” in International Conference on Human-Computer Interaction, Springer, 2022, pp. 548–563

    2022

  26. [34]

    Towards Open and Expandable Cognitive AI Architectures for Large- Scale Multi-Agent Human-Robot Collaborative Learning,

    G. T. Papadopoulos, M. Antona, and C. Stephanidis, “Towards Open and Expandable Cognitive AI Architectures for Large- Scale Multi-Agent Human-Robot Collaborative Learning,” IEEE Access , vol. 9, pp. 73 890–73 909, 2021, ISSN : 2169- 3536

    2021

  27. [35]

    Ontology for autonomous robotics,

    J. I. Olszewska, M. Barreto, J. Bermejo-Alonso, et al. , “Ontology for autonomous robotics,” in 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Aug. 2017, pp. 189–194

    2017

  28. [36]

    Survey on Hand Gesture Recognition from Visual Input

    M. Linardakis, I. Varlamis, and G. T. Papadopoulos. “Survey on Hand Gesture Recognition from Visual Input.” arXiv: 2501.11992 [cs]. (Jan. 21, 2025)

    2025 arXiv

  29. [37]

    Large language models for human–robot interaction: A review,

    C. Zhang, J. Chen, J. Li, Y . Peng, and Z. Mao, “Large language models for human–robot interaction: A review,” Biomimetic Intelligence and Robotics, vol. 3, no. 4, p. 100 131, Dec. 1, 2023, ISSN : 2667-3797

    2023

  30. [38]

    ProgPrompt: Generating Situated Robot Task Plans using Large Language Models,

    I. Singh, V . Blukis, A. Mousavian, et al. , “ProgPrompt: Generating Situated Robot Task Plans using Large Language Models,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , May 2023, pp. 11 523–11 530

    2023

  31. [39]

    Enhanced Cognition for Adaptive Human-Robot Collaboration,

    A. Umbrico, M. Anasagasti, S.-O. Bezrucav, et al., “Enhanced Cognition for Adaptive Human-Robot Collaboration,” in 2022 IEEE 27th International Conference on Emerging Technolo- gies and Factory Automation (ETFA) , Sep. 2022, pp. 1–7

    2022

  32. [40]

    Super Resolution for Augmented Reality Applications,

    V . Li, G. Amponis, J.-C. Nebel, et al., “Super Resolution for Augmented Reality Applications,” in IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), May 2022, pp. 1–6

    2022

  33. [41]

    What do emergency services and authorities need from society to better handle disasters?

    S. Elkady, J. Hernantes, M. Mu ˜noz, and L. Labaka, “What do emergency services and authorities need from society to better handle disasters?” International Journal of Disaster Risk Reduction, vol. 72, p. 102 864, Apr. 1, 2022, ISSN : 2212- 4209

    2022

  34. [42]

    Markuse, Contains modified Copernicus Sentinel data [2023], processed by Pierre Markuse, May 6, 2023

    P. Markuse, Contains modified Copernicus Sentinel data [2023], processed by Pierre Markuse, May 6, 2023. [Online]. Available: https://commons.wikimedia.org/wiki/File:Big fire in the Sverdlovsk Oblast, Russia %28Lat- 56 682, Lng- 63 472%29 - 6 May 2023 %2852880768096%29.jpg

    2023

  35. [43]

    Wright, Raising the Flood Defences, Barton Haven, Lin- colnshire, England

    D. Wright, Raising the Flood Defences, Barton Haven, Lin- colnshire, England. Sep. 20, 2005. [Online]. Available: https: / / commons . wikimedia . org / wiki / File : Raisingthe Flood Defences - geograph.org.uk - 72827.jpg

    2005

  36. [44]

    Het was allemaal bizar

    M. van Defensie, Nederlands: “Het was allemaal bizar. ”

  37. [2015]

    Available: https://commons.wikimedia.org/ wiki/File:USAR Nepal 2015 01.jpg

    [Online]. Available: https://commons.wikimedia.org/ wiki/File:USAR Nepal 2015 01.jpg

    2015

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.