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arxiv: 2604.12486 · v1 · submitted 2026-04-14 · 💻 cs.RO

DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation

Sunyao Zhou , Yunzi Wu , Tianhang Wang , Xinhai Li , Guang Chen , Lizheng Liu , Chenjia Bai , Xuelong Li This is my paper

Pith reviewed 2026-05-10 15:59 UTC · model grok-4.3

classification 💻 cs.RO
keywords collaborative vision-language navigationmulti-robot systemsevent-triggered dialoguedynamic task allocationdecentralized frameworklong-horizon tasksadaptive coordination
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The pith

DeCoNav improves multi-robot collaborative navigation by using event-triggered dialogue for dynamic task reallocation and replanning.

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

This paper presents DeCoNav as a decentralized framework for long-horizon collaborative vision-language navigation among multiple robots. The approach triggers dialogue between robots only when events such as new evidence, uncertainty, or conflicts occur, allowing them to exchange compact semantic states and dynamically reassign subgoals. This replaces static coordination with adaptive, synchronized execution on a shared timeline without a central controller. The framework is evaluated on DeCoNavBench, which includes 1,213 tasks across 176 scenes, showing substantial gains in the rate at which both robots succeed.

Core claim

DeCoNav is a decentralized framework that couples event-triggered dialogue with dynamic task allocation and replanning to enable real-time adaptive coordination for multi-robot long-horizon VLN tasks. Robots share compact semantic states via dialogue when informative events arise, enabling dynamic reassignment of subgoals and replanning while maintaining synchronized execution. This is demonstrated through implementation in DeCoNavBench, where it achieves a 69.2% improvement in both-success rate over existing methods.

What carries the argument

Event-triggered dialogue that activates on new evidence, uncertainty or conflicts to support dynamic task reallocation and synchronized replanning among robots.

Load-bearing premise

That event-triggered dialogue using compact semantic states can reliably resolve uncertainties, conflicts, and new evidence in real time without introducing excessive communication delays or miscommunications that would degrade synchronized execution.

What would settle it

Comparing DeCoNav performance against static coordination baselines in scenarios with simulated communication noise or delays to check if the both-success rate improvement is lost.

Figures

Figures reproduced from arXiv: 2604.12486 by Chenjia Bai, Guang Chen, Lizheng Liu, Sunyao Zhou, Tianhang Wang, Xinhai Li, Xuelong Li, Yunzi Wu.

Figure 1
Figure 1. Figure 1: Overview of DeCoNav and DeCoNavBench. Module 1: The ROVE pipeline constructs verified episodes through rule-based inference, VLM classification, human adjudication (RTSA), and triple-gate target verification (TriGate). Module 2: DeCoNav coordinates dual-robot execution via three coupled components: Semantic Visual Bus (SVB) for compact state exchange, Event-driven Dialogue Replanning (EDR) for online subta… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the TriGate target verification pipeline. Each candidate [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of dynamic subtask reassignment in DeCoNav. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Real-robot deployment on a collaborative object transport task. Robot 1 and Robot 2 operate as equal peers and execute decomposed subtasks [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

Long-horizon collaborative vision-language navigation (VLN) is critical for multi-robot systems to accomplish complex tasks beyond the capability of a single agent. CoNavBench takes a first step by introducing the first collaborative long-horizon VLN benchmark with relay-style multi-robot tasks, a collaboration taxonomy, along with graph-grounded generation and evaluation to model handoffs and rendezvous in shared environments. However, existing benchmarks and evaluations often do not enforce strictly synchronized dual-robot rollout on a shared world timeline, and they typically rely on static coordination policies that cannot adapt when new cross-agent evidence emerges. We present Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation (DeCoNav), a decentralized framework that couples event-triggered dialogue with dynamic task allocation and replanning for real-time, adaptive coordination. In DeCoNav, robots exchange compact semantic states via dialogue without a central controller. When informative events such as new evidence, uncertainty, or conflicts arise, dialogue is triggered to dynamically reassign subgoals and replan under synchronized execution. Implemented in DeCoNavBench with 1,213 tasks across 176 HM3D scenes, DeCoNav improves the both-success rate (BSR) by 69.2%, demonstrating the effectiveness of dialogue-driven, dynamically reallocated planning for multi-robot collaboration.

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

3 major / 2 minor

Summary. The manuscript introduces DeCoNav, a decentralized framework for long-horizon collaborative vision-language navigation that couples event-triggered dialogue (exchanging compact semantic states) with dynamic task allocation and replanning. Robots operate without a central controller and trigger dialogue on new evidence, uncertainty, or conflicts to reassign subgoals while maintaining strictly synchronized dual-robot execution on a shared world timeline. The work also presents DeCoNavBench, a benchmark with 1,213 tasks across 176 HM3D scenes that enforces graph-grounded handoffs and rendezvous, and reports a 69.2% improvement in both-success rate (BSR).

Significance. If the central empirical claim holds after addressing the evaluation gaps, the work would be significant for multi-robot VLN by demonstrating that dialogue-driven, decentralized coordination can outperform static policies in long-horizon settings. The introduction of DeCoNavBench with its collaboration taxonomy and synchronized rollout protocol is a clear contribution that enables future research on adaptive multi-agent navigation. The approach of using compact semantic states for event-triggered replanning is a practical step toward real-time collaboration without centralized control.

major comments (3)
  1. [Abstract] Abstract: The headline claim of a 69.2% BSR improvement is presented without any baseline methods, quantitative comparisons, error bars, statistical tests, or details on how BSR is computed or aggregated across the 1,213 tasks. This directly undermines verification of the central result.
  2. [Framework and Evaluation] Framework and Evaluation sections: The description of event-triggered dialogue assumes it resolves uncertainties and conflicts without introducing communication delays or breaking synchronized execution on the shared timeline, yet no quantitative account of dialogue frequency, modeled latency, bandwidth usage, or an ablation isolating the cost of triggering/exchanging messages is provided. This is load-bearing for the claim that DeCoNav outperforms static baselines under strictly synchronized rollout.
  3. [Results] Results: No ablation studies are described that separate the contribution of dynamic reallocation via dialogue from the baseline synchronization mechanism or that test robustness when dialogue events occur frequently in long-horizon tasks. Without these, the 69.2% gain cannot be confidently attributed to the proposed mechanism rather than an idealized zero-latency communication model.
minor comments (2)
  1. [Abstract] The abstract introduces 'both-success rate (BSR)' and 'DeCoNavBench' without a one-sentence definition or scope; adding these would improve immediate readability for readers unfamiliar with the benchmark.
  2. [Methods] Notation for compact semantic states and event triggers could be formalized with a short table or pseudocode in the methods to clarify what information is exchanged during dialogue.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the detailed and constructive feedback on our manuscript. We address each of the major comments point by point below, committing to revisions that enhance the clarity and rigor of the evaluation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim of a 69.2% BSR improvement is presented without any baseline methods, quantitative comparisons, error bars, statistical tests, or details on how BSR is computed or aggregated across the 1,213 tasks. This directly undermines verification of the central result.

    Authors: We agree that the abstract, due to its brevity, does not include these details. However, the full paper in the Results section provides comparisons to several baselines including static allocation policies and centralized coordination methods, along with error bars from 5 random seeds and p-values from statistical tests. BSR is defined as the fraction of tasks where both agents succeed in their assigned sub-tasks and meet at the rendezvous point, macro-averaged over the 1,213 tasks. We will update the abstract to include a short phrase referencing the main baseline and the BSR computation method. revision: yes

  2. Referee: [Framework and Evaluation] Framework and Evaluation sections: The description of event-triggered dialogue assumes it resolves uncertainties and conflicts without introducing communication delays or breaking synchronized execution on the shared timeline, yet no quantitative account of dialogue frequency, modeled latency, bandwidth usage, or an ablation isolating the cost of triggering/exchanging messages is provided. This is load-bearing for the claim that DeCoNav outperforms static baselines under strictly synchronized rollout.

    Authors: The framework assumes idealized instantaneous communication to focus on the coordination benefits, as the compact semantic states are designed to be low-bandwidth. We will add quantitative results on average dialogue frequency per task, modeled latency using standard wireless models, and bandwidth estimates. Additionally, we will include an ablation that measures performance degradation under simulated delays to validate the synchronized execution claim. revision: yes

  3. Referee: [Results] Results: No ablation studies are described that separate the contribution of dynamic reallocation via dialogue from the baseline synchronization mechanism or that test robustness when dialogue events occur frequently in long-horizon tasks. Without these, the 69.2% gain cannot be confidently attributed to the proposed mechanism rather than an idealized zero-latency communication model.

    Authors: We will perform and report additional ablation studies in the revised manuscript. Specifically, we will compare DeCoNav against a synchronized but static allocation variant to isolate the dynamic reallocation benefit, and test scenarios with artificially increased dialogue frequency to assess robustness in long-horizon settings. These will help attribute the gains more precisely. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical implementation and benchmark results

full rationale

The paper introduces DeCoNav as a decentralized framework coupling event-triggered dialogue with dynamic task allocation, then reports its implementation and evaluation on the DeCoNavBench benchmark (1,213 tasks across 176 scenes) yielding a 69.2% BSR gain. No mathematical derivations, equations, fitted parameters, or first-principles predictions are claimed; the performance result is presented strictly as an outcome of the implemented system under synchronized rollout. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the provided text. The central claim therefore remains an independent empirical observation rather than a reduction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits visibility into internal assumptions; the central claim rests on the unstated premise that compact semantic state exchange via dialogue is sufficient and timely for coordination.

axioms (1)
  • domain assumption Robots can exchange compact semantic states via dialogue to resolve uncertainties and conflicts without a central controller
    Invoked when describing the decentralized framework and event-triggered mechanism.

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