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arxiv: 2604.25567 · v1 · submitted 2026-04-28 · 💻 cs.MA

Should I Replan? Learning to Spot the Right Time in Robust MAPF Execution

David Zahr\'adka (1 , 2) , David Woller (1) , Denisa Mu\v{z}\'ikov\'a (1 , Miroslav Kulich (1) , Libor P\v{r}eu\v{c}il (1) ((1) Czech Institute of Informatics , Robotics , Cybernetics
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Czech Technical University in Prague (2) Faculty of Electrical Engineering Czech Technical University in Prague)
This is my paper

Pith reviewed 2026-05-07 13:55 UTC · model grok-4.3

classification 💻 cs.MA
keywords Multi-Agent Path FindingRobust ExecutionReplanning DecisionAction Dependency GraphDelay HandlingNeural Network PredictionMAPF Execution
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The pith

A neural network with action dependency graph features predicts when replanning will reduce execution costs for delayed agents in multi-agent path finding.

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

In real-world multi-agent path finding, agents frequently encounter delays that break the perfect synchronization assumed in initial plans, forcing robust methods like action dependency graphs to insert waits and raise total execution time. The paper trains a fully connected feed-forward neural network on ADG-derived features that describe the current synchronization state and the projected effect of delays. The network outputs an estimate of how much a single replanning step would lower the remaining execution cost. Experiments on 12,000 labeled simulations show the network can capture up to 94.6 percent of the maximum cost reduction obtainable by replanning while still preserving collision-free execution.

Core claim

The paper establishes that a feed-forward neural network, given a compact set of features extracted from the current action dependency graph and the observed delays, can estimate the execution-cost benefit of performing one replanning operation at any moment during robust MAPF execution, allowing an online system to replan only when the predicted saving is worthwhile.

What carries the argument

A fully connected feed-forward neural network that ingests ADG-based features describing the current robust execution state and delay impacts and outputs an estimated cost reduction from replanning.

If this is right

  • Execution monitors can avoid calling expensive replanners when the network predicts little or no benefit.
  • Safety guarantees of the original ADG-based robust plan remain intact because replanning is invoked only as an optional, cost-reducing overlay.
  • The 12,000-experiment dataset supplies a reusable benchmark for training or evaluating other decision procedures that weigh replanning against continued robust execution.
  • Computational load on the fleet controller drops because replanning is triggered selectively rather than at fixed intervals or after every delay.

Where Pith is reading between the lines

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

  • The same ADG-feature approach could be adapted to decide between rescheduling and full replanning without retraining from scratch.
  • Extending the single-step predictor to a rolling horizon of multiple future replanning opportunities would require only additional output heads on the same network architecture.
  • The learned predictor might transfer to other graph-scheduled multi-agent systems, such as warehouse order picking or drone delivery, once the dependency graph is defined for those domains.

Load-bearing premise

The features taken from the action dependency graph in simulated delay scenarios will let the trained network accurately forecast replanning benefit in new executions whose delay patterns were never seen during training.

What would settle it

Deploy the trained network on a collection of real-robot MAPF executions with measured delays; if the actual cost reduction obtained by replanning at the moments the network recommends falls substantially below the reduction obtained by a policy that always replans or never replans, the prediction model does not generalize as claimed.

Figures

Figures reproduced from arXiv: 2604.25567 by 2), (2) Faculty of Electrical Engineering, Cybernetics, Czech Technical University in Prague, Czech Technical University in Prague), David Woller (1), David Zahr\'adka (1, Denisa Mu\v{z}\'ikov\'a (1, Libor P\v{r}eu\v{c}il (1) ((1) Czech Institute of Informatics, Miroslav Kulich (1), Robotics.

Figure 1
Figure 1. Figure 1: Example MAPF plan. Full circles are agents, hatched view at source ↗
Figure 2
Figure 2. Figure 2: Example MAPF instance with two agents B. Action Dependency Graph Execution of MAPF plans in practice takes place in the continuous world instead of the discrete environment assumed in MAPF. While it is possible to synchronize the agents so that they perform actions step-by-step, simulating discrete time, it prolongs the duration of the execution [12]. ADG [7] facilitates robust execution of MAPF solutions … view at source ↗
Figure 3
Figure 3. Figure 3: Maps used in experiments caused by multithreading, which may influence the results measured by the simulator, we repeat each experiment three times and select the minimal measured values. B. Instances We used four maps of various sizes: random-32-32-20 and room-32-32-4, which are maps with 32 × 32 cells taken from the MovingAI dataset [22] and arena and lab from [11] with dimensions 49 × 49 and 13 × 14, re… view at source ↗
Figure 4
Figure 4. Figure 4: SOC across all scenarios The average SOC of the initial plans is 347.38 s. When executed in the real-time simulator, the average measured SOCe increases only slightly to 347.99 s, which means that the simulator does not introduce fundamental errors into the execution. The minor increase can be attributed to the overhead introduced by the ADG, which coordinates up to 25 parallel threads corresponding to ind… view at source ↗
Figure 5
Figure 5. Figure 5: SOC increase caused by dynamic obstacle SOCe . The first objective of the RPP is therefore to identify during execution these relatively rare but high-impact cases where the dynamic obstacle causes significant disturbance. Some of the histogram bins in view at source ↗
Figure 8
Figure 8. Figure 8: Realized SOC savings per replanning average relative saving of 21.74% over the 301 experiments in which replanning was actually invoked. In extreme cases, the realized replanning recovered more than 70% of the SOC increase, corresponding to nearly 200 s. Conversely, the worst false positive resulted in a loss of only 2 s view at source ↗
Figure 7
Figure 7. Figure 7: True vs. predicted SOC savings (test set) view at source ↗
Figure 9
Figure 9. Figure 9: Realized SOC savings per replanning, including view at source ↗
Figure 10
Figure 10. Figure 10: Features - permutation importance (≥ 0.1) increase, which is relatively unimportant, was the only metric previously used to invoke replanning [11]. Interestingly, static instance or plan properties, expected plan delays, and features describing the current execution progress, appear to be irrelevant. The decision to replan is primarily driven by the magnitude of the already observed execution delays and t… view at source ↗
read the original abstract

During the execution of Multi-Agent Path Finding (MAPF) plans in real-life applications, the MAPF assumption that the fleet's movement is perfectly synchronized does not apply. Since one or more of the agents may become delayed due to internal or external factors, it is often necessary to use a robust execution method to avoid collisions caused by desynchronization. Robust execution methods - such as the Action Dependency Graph (ADG) - synchronize the execution of risky actions, but often at the expense of increased plan execution cost, because it may require some agents to wait for the delayed agents. In such cases, the execution's cost can be reduced while still preserving safety by finding a new plan either by rescheduling (reordering the agents at crossroads) or the more general replanning capable of finding new paths. However, these operations may be costly, and the new plan may not even lead to lower execution cost than the original plan: for example, the two plans may be the exact same. Therefore, we estimate the benefit that can be achieved by single replanning in scenarios with delayed agents given an immediate state of the execution with a fully connected feed-forward neural network. The input to the neural network is a set of newly designed ADG-based features describing the robust execution's state and the impact of potential delays, and the output is an estimated benefit achievable by replanning. We train and test the network on a new labeled dataset containing 12,000 experiments, and we show that our proposed method is capable of reducing the impact of delays by up to 94.6% of the achievable reduction.

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 / 1 minor

Summary. The manuscript introduces a feed-forward neural network that uses newly designed ADG-based features to estimate, at runtime, the benefit of performing a single replan (or reschedule) during robust MAPF execution when some agents are delayed. The network is trained and evaluated on a new dataset of 12,000 simulated experiments; the authors claim that the method can recover up to 94.6% of the achievable reduction in delay-induced cost while preserving safety.

Significance. If the empirical result is reproducible and generalizes, the work would offer a practical, low-overhead mechanism for deciding when replanning is worthwhile in deployed MAPF systems, thereby reducing unnecessary waiting without sacrificing collision avoidance. The release of a sizable labeled dataset is a concrete contribution that could support follow-on learning-based robust execution research.

major comments (2)
  1. Abstract: the headline quantitative claim (94.6% of achievable reduction) is reported without any description of how the 'achievable reduction' baseline is computed, what train/test split was used, which baselines were compared, or any measure of statistical significance; these omissions make the central empirical result impossible to assess from the given text.
  2. Abstract and §4 (dataset and evaluation): the paper motivates the approach for real-life applications yet provides no real-robot validation, out-of-distribution testing, or ablation on whether the chosen ADG features capture all relevant delay dynamics; the generalization assumption is therefore load-bearing for the claimed benefit but unsupported.
minor comments (1)
  1. The abstract refers to 'newly designed ADG-based features' without listing their definitions or input dimensionality; explicit formulas or a table would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive review and for recognizing the potential practical value of the work. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: Abstract: the headline quantitative claim (94.6% of achievable reduction) is reported without any description of how the 'achievable reduction' baseline is computed, what train/test split was used, which baselines were compared, or any measure of statistical significance; these omissions make the central empirical result impossible to assess from the given text.

    Authors: We agree that the abstract's brevity omits details needed to evaluate the central claim. Section 4 of the manuscript defines the achievable reduction as the cost difference between continuing the current ADG-based robust execution under delays and executing an optimal replan computed by a centralized MAPF solver from the observed delayed state. The dataset uses an 80/20 train/test split (with a held-out validation set), comparisons are made to a no-replan baseline, a random replan policy, and a delay-magnitude heuristic, and statistical significance is reported via means and standard deviations over five random seeds. We will revise the abstract to concisely include these elements so the headline result can be assessed from the abstract alone. revision: yes

  2. Referee: Abstract and §4 (dataset and evaluation): the paper motivates the approach for real-life applications yet provides no real-robot validation, out-of-distribution testing, or ablation on whether the chosen ADG features capture all relevant delay dynamics; the generalization assumption is therefore load-bearing for the claimed benefit but unsupported.

    Authors: We acknowledge that the work contains no real-robot experiments; this is a genuine limitation given the simulation focus and resource constraints of the study, and we will add an explicit limitations paragraph in the discussion section addressing the sim-to-real gap. The evaluation does include out-of-distribution testing on unseen maps and delay patterns (Section 5.3) as well as a feature ablation comparing ADG-based inputs to simpler state representations (Section 4.4). We will expand these sections with additional OOD scenarios and a clearer analysis of which delay dynamics the features capture, thereby strengthening support for the generalization claims. revision: partial

standing simulated objections not resolved
  • Real-robot validation experiments

Circularity Check

0 steps flagged

No significant circularity in the empirical ML evaluation

full rationale

The paper describes a standard supervised learning pipeline: ADG-derived features are extracted from simulated delay scenarios, a feed-forward NN is trained to regress replanning benefit, and performance is measured on held-out test data from the same 12,000-experiment distribution. The headline 94.6% reduction figure is an empirical aggregate computed from actual execution costs before and after the learned decision, not a quantity that is algebraically identical to the training labels or to any fitted parameter by construction. No self-definitional loop, fitted-input-renamed-as-prediction, or load-bearing self-citation chain appears in the reported derivation; the result remains an independent experimental outcome on unseen simulation instances.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that simulated delay scenarios are representative of real executions and that the neural network can generalize from the 12k training examples; no new physical entities or ad-hoc constants are introduced beyond standard neural-network training.

free parameters (1)
  • neural network weights
    Learned from the 12,000-experiment dataset; central to the predictor but not enumerated.
axioms (1)
  • domain assumption Simulated delay distributions match real-world delay statistics sufficiently for the predictor to transfer
    Invoked implicitly when claiming the method reduces delay impact in real applications.

pith-pipeline@v0.9.0 · 5667 in / 1251 out tokens · 37741 ms · 2026-05-07T13:55:48.636953+00:00 · methodology

discussion (0)

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