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arxiv: 2604.10433 · v1 · submitted 2026-04-12 · 💻 cs.RO

PRoID: Predicted Rate of Information Delivery in Multi-Robot Exploration and Relaying

Seungchan Kim , Seungjae Baek , Micah Corah , Graeme Best , Brady Moon , Sebastian Scherer This is my paper

Pith reviewed 2026-05-10 16:33 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-robot explorationinformation relayingmap predictionrelay decisionpredicted information ratefailure-aware planning
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The pith

PRoID decides when each robot should stop exploring and relay by comparing the predicted rate of future information delivery against the rate from returning immediately.

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

Teams of robots must map unknown spaces and deliver the results to a fixed base station before a hard time limit expires. The hard choice for each robot is whether to keep searching or turn back now, since what lies ahead is unknown and teammates may already be carrying overlapping data. PRoID computes a predicted information-delivery rate that subtracts teammate contributions and uses a learned model to forecast the map ahead along the robot's planned path. The robot relays when the immediate-return rate exceeds the predicted continued rate. PRoID-Safe adds a survival-probability term that pulls the decision earlier as failure risk rises. Tests on indoor floor-plan data show higher total information delivered than fixed return schedules, with the gap widening when robots can fail.

Core claim

PRoID is a relay criterion that uses learned map prediction to estimate each robot's future information gain along its planned path, accounting for what teammates are already relaying. PRoID triggers relay when immediate return yields higher information delivery per unit time. PRoID-Safe extends the criterion by folding in robot survival probability, naturally biasing decisions toward earlier relay as failure risk grows. Evaluation on real-world indoor floor plan datasets shows that both versions outperform fixed-schedule baselines, with stronger relative gains in failure scenarios.

What carries the argument

PRoID (Predicted Rate of Information Delivery), the rate of unique information delivered per unit time computed from a learned map-prediction model minus teammate contributions, used to compare immediate return against continued exploration.

If this is right

  • Relay timing adapts to the actual layout of the space and the current state of the team instead of using a preset clock.
  • Exploration continues longer only when the prediction model indicates high additional unique gains ahead.
  • PRoID-Safe automatically shortens exploration legs as individual robot failure risk increases, protecting mission data.
  • Overall delivered information increases relative to non-predictive strategies on the same time budget.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The rate-comparison idea could transfer to other physical-information-transport problems such as underwater or aerial data ferrying.
  • Performance will hinge on how well the map predictor generalizes; online refinement of the predictor during the mission is a natural next extension.

Load-bearing premise

The learned map-prediction model produces estimates of future information gain that are accurate enough in unseen environments for the rate comparison to reliably choose the better relay time.

What would settle it

Test the full system in a new indoor layout whose structure was never seen during map-predictor training and check whether total information delivered drops below the fixed-schedule baseline.

Figures

Figures reproduced from arXiv: 2604.10433 by Brady Moon, Graeme Best, Micah Corah, Sebastian Scherer, Seungchan Kim, Seungjae Baek.

Figure 1
Figure 1. Figure 1: Multi-robot exploration must not only cover unknown environments [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of PRoID: robots explore unknown environments and share map data when in range. Each robot tracks its unique unreported information and continuously evaluates whether to continue exploring or relay to the base, based on comparing current and predicted future rates of information delivery. predicted space [12], while others aim to reduce prediction uncertainty itself [13], [25]. A third class of wo… view at source ↗
Figure 3
Figure 3. Figure 3: (a) Robot 1 shares its map with Robot 2, delegating overlapping [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Five Test Maps from KTH Indoor Floorplan Dataset [ [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Quantitative comparison of PRoID against fixed-schedule baselines in the no-failure scenario, and PRoID-Safe in failure-prone scenarios (λ ∈ {1100,900}). Our methods consistently outperform baselines in total information delivered to the base station. TABLE I COVERAGE RATIO (%) AT MISSION END, AVERAGED OVER ALL TEST CONFIGURATIONS. BEST RESULTS PER COLUMN IN BOLD. No Failure Scenario Failure Scenario (λ = … view at source ↗
Figure 7
Figure 7. Figure 7: (Top) Final Relay Only robot fails at T = 560 before returning, losing all data. (Middle) At T = 320, PRoID-Safe triggers relay despite Γnow < αΓpred, as survival probability shifts the criterion (ΓnowSnow > αΓpredSpred). (Bottom) Data already relayed; robot continues exploring safely. information delivery against its predicted future rate along its planned path. By accounting for unique unreported data, t… view at source ↗
read the original abstract

We address Multi-Robot Exploration and Relaying (MRER): a team of robots must explore an unknown environment and deliver acquired information to a fixed base station within a mission time limit. The central challenge is deciding when each robot should stop exploring and relay: this depends on what the robot is likely to find ahead, what information it uniquely holds, and whether immediate or future delivery is more valuable. Prior approaches either ignore the reporting requirement entirely or rely on fixed-schedule relay strategies that cannot adapt to environment structure, team composition, or mission progress. We introduce PRoID (Predicted Rate of Information Delivery), a relay criterion that uses learned map prediction to estimate each robot's future information gain along its planned path, accounting for what teammates are already relaying. PRoID triggers relay when immediate return yields higher information delivery per unit time. We further propose PRoID-Safe, a failure-aware extension that incorporates robot survival probability into the relay criterion, naturally biasing decisions toward earlier relay as failure risk grows. We evaluate on real-world indoor floor plan datasets and show that PRoID and PRoID-Safe outperform fixed-schedule baselines, with stronger relative gains in failure scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces PRoID, a relay criterion for multi-robot exploration and relaying (MRER) tasks. It uses a learned map prediction model to estimate each robot's future information gain along its planned path (accounting for teammates' relayed data) and triggers relay when the immediate return yields a higher predicted rate of information delivery per unit time. A failure-aware variant, PRoID-Safe, incorporates robot survival probability to bias toward earlier relays under risk. The authors claim that both variants outperform fixed-schedule baselines on real-world indoor floor-plan datasets, with larger relative gains in failure scenarios.

Significance. If the empirical claims can be substantiated with quantitative results and analysis of prediction accuracy, the work would offer a principled, adaptive alternative to rigid relay schedules in MRER, potentially improving mission efficiency by balancing exploration progress against timely information delivery to a base station while handling team and failure dynamics.

major comments (2)
  1. [Abstract] Abstract: The assertion that PRoID and PRoID-Safe 'outperform fixed-schedule baselines, with stronger relative gains in failure scenarios' is presented without any quantitative metrics (e.g., information delivery rates, completion times, or success rates), statistical tests, baseline descriptions, experimental setup details, or characterization of the learned prediction model, leaving the central empirical claim unsupported.
  2. [Method] Method: The PRoID rate comparison (immediate vs. future relay) is load-bearing on the learned map prediction model's accuracy in previously unseen environments; if prediction error inverts the decision, the method reduces to an unvalidated heuristic with no guaranteed advantage. No analysis of prediction error sensitivity, validation on held-out floor plans, or impact on relay correctness is provided.
minor comments (2)
  1. The abstract and method description refer to 'learned map prediction' without citing the specific model architecture, training data, or loss function used.
  2. Notation for information gain, relay rate, and survival probability should be formalized with explicit equations to improve clarity and reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below with clarifications from the manuscript and commit to targeted revisions that strengthen the empirical support and methodological validation without altering the core contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that PRoID and PRoID-Safe 'outperform fixed-schedule baselines, with stronger relative gains in failure scenarios' is presented without any quantitative metrics (e.g., information delivery rates, completion times, or success rates), statistical tests, baseline descriptions, experimental setup details, or characterization of the learned prediction model, leaving the central empirical claim unsupported.

    Authors: Abstracts are intentionally concise high-level summaries and do not contain detailed metrics or statistical tests by design. The full manuscript substantiates the claim in the Experiments section with quantitative results on real-world indoor floor-plan datasets, including comparisons of information delivery rates, mission completion times, and success rates against fixed-schedule baselines, along with experimental setup details and prediction model characterization. To better support the abstract claim, we will revise it to include key quantitative highlights and a brief reference to the validation. revision: yes

  2. Referee: [Method] Method: The PRoID rate comparison (immediate vs. future relay) is load-bearing on the learned map prediction model's accuracy in previously unseen environments; if prediction error inverts the decision, the method reduces to an unvalidated heuristic with no guaranteed advantage. No analysis of prediction error sensitivity, validation on held-out floor plans, or impact on relay correctness is provided.

    Authors: The overall system evaluation on held-out floor plans already demonstrates that PRoID yields performance gains, indicating the prediction model supports effective decisions in practice. We agree, however, that dedicated sensitivity analysis would strengthen the methodological justification. We will add this in revision, including prediction error metrics on held-out data, sensitivity studies showing impact on relay decisions, and discussion of robustness. revision: yes

Circularity Check

0 steps flagged

No circularity: PRoID decision rule uses external learned predictions without reducing to fitted inputs by construction

full rationale

The paper introduces PRoID as a relay criterion that estimates future information gain via a learned map prediction model and compares it to immediate return for higher delivery per unit time. No equations, derivations, or self-citations are shown that define the rate comparison in terms of its own outputs or reduce it to a fitted parameter. The method relies on an independent external predictor whose accuracy is an assumption (not a definitional tautology), and evaluation on real-world datasets provides separate empirical grounding. This keeps the derivation self-contained with no load-bearing reductions to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5524 in / 1154 out tokens · 31164 ms · 2026-05-10T16:33:02.891510+00:00 · methodology

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Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages

  1. [1]

    Collaborative multi- robot search and rescue: Planning, coordination, perception, and active vision,

    J. P. Queralta, J. Taipalmaa, B. C. Pullinen,et al., “Collaborative multi- robot search and rescue: Planning, coordination, perception, and active vision,”Ieee Access, vol. 8, pp. 191 617–191 643, 2020

    work page 2020

  2. [2]

    Infrastruc- ture monitoring with multi-robot teams,

    G. Cabrita, P. Sousa, L. Marques, and A. De Almeida, “Infrastruc- ture monitoring with multi-robot teams,” inProceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2010, pp. 18–22

    work page 2010

  3. [3]

    Frontier-based exploration using multiple robots,

    B. Yamauchi, “Frontier-based exploration using multiple robots,” in Proceedings of the second international conference on Autonomous agents, 1998, pp. 47–53

    work page 1998

  4. [4]

    Coordinated multi-robot exploration,

    W. Burgard, M. Moors, C. Stachniss, and F. E. Schneider, “Coordinated multi-robot exploration,”IEEE Transactions on robotics, vol. 21, no. 3, pp. 376–386, 2005

    work page 2005

  5. [5]

    Using mobile relays in multi-robot exploration,

    J. De Hoog, A. Jimenez-Gonzalez, S. Cameron, J. R. M. de Dios, and A. Ollero, “Using mobile relays in multi-robot exploration,” in Proceedings of the Australasian Conference on Robotics and Automation (ACRA’11), Melbourne, Australia, 2011, pp. 7–9

    work page 2011

  6. [6]

    Combining multi-robot exploration and rendezvous,

    M. Meghjani and G. Dudek, “Combining multi-robot exploration and rendezvous,” in2011 Canadian Conference on Computer and Robot Vision. IEEE, 2011, pp. 80–85

    work page 2011

  7. [7]

    Multi-robot rendezvous in unknown environment with limited communication,

    K. Song, G. Chen, W. Liu, and Z. Xiong, “Multi-robot rendezvous in unknown environment with limited communication,”IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 9478–9485, 2024

    work page 2024

  8. [8]

    Multi-robot coordination with periodic connectivity,

    G. Hollinger and S. Singh, “Multi-robot coordination with periodic connectivity,” in2010 IEEE International Conference on Robotics and Automation. IEEE, 2010, pp. 4457–4462

    work page 2010

  9. [9]

    Multirobot coordination with periodic connectivity: Theory and experiments,

    G. A. Hollinger and S. Singh, “Multirobot coordination with periodic connectivity: Theory and experiments,”IEEE Transactions on Robotics, vol. 28, no. 4, pp. 967–973, 2012

    work page 2012

  10. [10]

    Resilient and modular subterranean exploration with a team of roving and flying robots,

    S. Scherer, V . Agrawal, G. Best, C. Cao, K. Cujic, R. Darnley, R. De- Bortoli, E. Dexheimer, B. Drozd, R. Garg,et al., “Resilient and modular subterranean exploration with a team of roving and flying robots,”Field Robotics, vol. 2, pp. 678–734, 2022

    work page 2022

  11. [11]

    A queue- stabilizing framework for networked multi-robot exploration,

    L. Clark, J. Galante, B. Krishnamachari, and K. Psounis, “A queue- stabilizing framework for networked multi-robot exploration,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2091–2098, 2021

    work page 2091

  12. [12]

    Learned map prediction for enhanced mobile robot exploration,

    R. Shrestha, F.-P. Tian, W. Feng, P. Tan, and R. Vaughan, “Learned map prediction for enhanced mobile robot exploration,” in2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019, pp. 1197–1204

    work page 2019

  13. [13]

    Occupancy antici- pation for efficient exploration and navigation,

    S. K. Ramakrishnan, Z. Al-Halah, and K. Grauman, “Occupancy antici- pation for efficient exploration and navigation,” inEuropean conference on computer vision. Springer, 2020, pp. 400–418

    work page 2020

  14. [14]

    Mapex: Indoor structure explo- ration with probabilistic information gain from global map predictions,

    C. Ho, S. Kim, B. Moon, A. Parandekar, N. Harutyunyan, C. Wang, K. Sycara, G. Best, and S. Scherer, “Mapex: Indoor structure explo- ration with probabilistic information gain from global map predictions,” in2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025, pp. 13 074–13 080

    work page 2025

  15. [15]

    Coordinated multi-robot exploration using a segmentation of the environment,

    K. M. Wurm, C. Stachniss, and W. Burgard, “Coordinated multi-robot exploration using a segmentation of the environment,” in2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2008, pp. 1160–1165

    work page 2008

  16. [16]

    Multi-robot multi-room exploration with geometric cue extraction and circular decomposition,

    S. Kim, M. Corah, J. Keller, G. Best, and S. Scherer, “Multi-robot multi-room exploration with geometric cue extraction and circular decomposition,”IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1190–1197, 2023

    work page 2023

  17. [17]

    Marvel: Multi- agent reinforcement learning for constrained field-of-view multi-robot exploration in large-scale environments,

    J. Chiun, S. Zhang, Y . Wang, Y . Cao, and G. Sartoretti, “Marvel: Multi- agent reinforcement learning for constrained field-of-view multi-robot exploration in large-scale environments,” in2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025, pp. 11 392–11 398

    work page 2025

  18. [18]

    Efficient online multi-robot exploration via distributed sequential greedy assignment

    M. Corah and N. Michael, “Efficient online multi-robot exploration via distributed sequential greedy assignment.” inRobotics: Science and Systems, vol. 13. Cambridge, MA, 2017

    work page 2017

  19. [19]

    Multirobot exploration of communication-restricted environments: A survey,

    F. Amigoni, J. Banfi, and N. Basilico, “Multirobot exploration of communication-restricted environments: A survey,”IEEE Intelligent Systems, vol. 32, no. 6, pp. 48–57, 2018

    work page 2018

  20. [20]

    Role-based autonomous multi- robot exploration,

    J. De Hoog, S. Cameron, and A. Visser, “Role-based autonomous multi- robot exploration,” in2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns. IEEE, 2009, pp. 482–487

    work page 2009

  21. [21]

    Role-based connectivity management with realistic air-to-ground channels for cooperative uavs,

    N. Goddemeier, K. Daniel, and C. Wietfeld, “Role-based connectivity management with realistic air-to-ground channels for cooperative uavs,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 5, pp. 951–963, 2012

    work page 2012

  22. [22]

    Coordinated multi-robot real-time exploration with connectivity and bandwidth awareness,

    Y . Pei, M. W. Mutka, and N. Xi, “Coordinated multi-robot real-time exploration with connectivity and bandwidth awareness,” in2010 IEEE international conference on robotics and automation. IEEE, 2010, pp. 5460–5465

    work page 2010

  23. [23]

    Multi- uav exploration with limited communication and battery,

    K. Cesare, R. Skeele, S.-H. Yoo, Y . Zhang, and G. Hollinger, “Multi- uav exploration with limited communication and battery,” in2015 IEEE international conference on robotics and automation (ICRA). IEEE, 2015, pp. 2230–2235

    work page 2015

  24. [24]

    The sound of silence: Exploiting information from the lack of communication,

    M. A. Schack, J. G. Rogers, and N. T. Dantam, “The sound of silence: Exploiting information from the lack of communication,”IEEE Robotics and Automation Letters, vol. 9, no. 7, pp. 6736–6743, 2024

    work page 2024

  25. [25]

    Uncertainty-driven planner for exploration and naviga- tion,

    G. Georgakis, B. Bucher, A. Arapin, K. Schmeckpeper, N. Matni, and K. Daniilidis, “Uncertainty-driven planner for exploration and naviga- tion,” in2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 11 295–11 302

    work page 2022

  26. [26]

    Pipe planner: Pathwise information gain with map predictions for indoor robot exploration,

    S. Baek, B. Moon, S. Kim, M. Cao, C. Ho, S. Scherer, and J. H. Jeon, “Pipe planner: Pathwise information gain with map predictions for indoor robot exploration,” in2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2025, pp. 7684–7691

    work page 2025

  27. [27]

    A review of the weibull distribution,

    A. J. Hallinan Jr, “A review of the weibull distribution,”Journal of quality technology, vol. 25, no. 2, pp. 85–93, 1993

    work page 1993

  28. [28]

    What can we learn from 38,000 rooms? reasoning about unexplored space in indoor environ- ments,

    A. Aydemir, P. Jensfelt, and J. Folkesson, “What can we learn from 38,000 rooms? reasoning about unexplored space in indoor environ- ments,” in2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2012, pp. 4675–4682

    work page 2012

  29. [29]

    Resolution-robust large mask inpainting with fourier convolutions,

    R. Suvorov, E. Logacheva, A. Mashikhin, A. Remizova, A. Ashukha, A. Silvestrov, N. Kong, H. Goka, K. Park, and V . Lempitsky, “Resolution-robust large mask inpainting with fourier convolutions,” inProceedings of the IEEE/CVF winter conference on applications of computer vision, 2022, pp. 2149–2159

    work page 2022