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arxiv: 2504.05477 · v1 · submitted 2025-04-07 · 💻 cs.RO

Trust Through Transparency: Explainable Social Navigation for Autonomous Mobile Robots via Vision-Language Models

Oluwadamilola Sotomi , Devika Kodi , Aliasghar Arab This is my paper

Pith reviewed 2026-05-22 20:21 UTC · model grok-4.3

classification 💻 cs.RO
keywords explainable navigationvision-language modelssocial roboticsrobot transparencyuser trustmultimodal explanationautonomous mobile robots
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The pith

A multimodal module lets robots generate natural language explanations for navigation choices using vision-language models and heat maps.

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

The paper develops a system for service robots that combines vision-language models with visual heat maps so the robot can describe its observations and decisions in plain language during movement through shared spaces. This setup aims to make the robot's behavior more transparent to nearby humans. Controlled tests with thirty participants found that most favored receiving these real-time explanations, which the authors link to greater reported trust and comprehension. The work validates the outputs against human judgments using confusion-matrix checks and presents the approach as effective for social navigation tasks.

Core claim

The paper claims that integrating vision-language models with heat maps produces a multimodal explainability module that lets robots perceive their surroundings, analyze them, and output natural-language summaries of their navigation decisions, resulting in measurable user preference for the explanations and improved agreement with human expectations as shown by study data and matrix analysis.

What carries the argument

The multimodal explainability module that fuses vision-language model outputs with heat maps to create natural language summaries of the robot's observations and path choices.

If this is right

  • Robots equipped with the module can produce real-time natural language explanations while moving in dynamic environments shared with people.
  • Majority preference for explanations appears in user data and correlates with higher reported trust and understanding.
  • Confusion-matrix validation confirms that the generated explanations align with human expectations of the robot's behavior.
  • The overall results indicate that adding such explainability increases interpretability of autonomous navigation.

Where Pith is reading between the lines

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

  • If the explanations remain accurate outside the lab, the module could reduce misunderstandings that lead to avoidance or conflict in crowded areas.
  • Long-term field deployments would need separate checks to confirm whether initial study preferences persist after repeated exposure.
  • The same module structure might be tested with additional sensor inputs to handle edge cases the current vision pipeline misses.

Load-bearing premise

The vision-language model outputs are assumed to faithfully represent the robot's actual decision process and that user preference shown in a controlled study will translate to sustained trust during real-world social navigation.

What would settle it

A follow-up trial in which participants navigate alongside the robot in an uncontrolled public space and report trust levels when explanations are withheld or deliberately mismatched to the actual path taken.

Figures

Figures reproduced from arXiv: 2504.05477 by Aliasghar Arab, Devika Kodi, Oluwadamilola Sotomi.

Figure 1
Figure 1. Figure 1: AMR approaches a social setting, demonstrating real-time explain [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A diagram showing the relationship between the nodes that make up the explainability module. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Test 1: User survey results [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Test 2: User survey results. Predicted Positive Predicted Negative Actual Positive TP: 82 FN: 15 Actual Negative FP: 20 TN: 79 TABLE III CONFUSION MATRIX SHOWING PERFORMANCE OF THE EXPLAINABILITY MODULE. TRUE POSITIVE (TP), FALSE POSITIVE (FP), FALSE NEGATIVE (FN), TRUE NEGATIVE (TN). improves performance and social acceptance in collabora￾tive environments between humans and robots. The survey results and… view at source ↗
read the original abstract

Service and assistive robots are increasingly being deployed in dynamic social environments; however, ensuring transparent and explainable interactions remains a significant challenge. This paper presents a multimodal explainability module that integrates vision language models and heat maps to improve transparency during navigation. The proposed system enables robots to perceive, analyze, and articulate their observations through natural language summaries. User studies (n=30) showed a preference of majority for real-time explanations, indicating improved trust and understanding. Our experiments were validated through confusion matrix analysis to assess the level of agreement with human expectations. Our experimental and simulation results emphasize the effectiveness of explainability in autonomous navigation, enhancing trust and interpretability.

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

1 major / 1 minor

Summary. The paper proposes a multimodal explainability module integrating vision-language models and heat maps to enable autonomous mobile robots to perceive, analyze, and articulate observations via natural language summaries during social navigation. It reports a user study (n=30) in which a majority preferred real-time explanations, interpreted as evidence of improved trust and understanding, and states that experiments were validated via confusion matrix analysis measuring agreement with human expectations. The work concludes that explainability enhances trust and interpretability in autonomous navigation.

Significance. If the central claims are supported by detailed evidence, the integration of VLMs for real-time natural-language explanations in social navigation could meaningfully advance transparency in human-robot interaction, a key barrier to deploying service robots. The empirical user-study component provides a direct test of user preference, which is a strength when properly documented. However, the current presentation leaves the validation approach underspecified, limiting the ability to evaluate whether the reported preference translates to genuine fidelity with the robot's decision process.

major comments (1)
  1. [Abstract] Abstract: the statement that 'experiments were validated through confusion matrix analysis to assess the level of agreement with human expectations' supplies no information on the matrix categories, the mapping from VLM text outputs to discrete classes, the collection of ground-truth labels, or the specific navigation actions or explanation qualities being classified. This analysis is presented as corroborating the user-study preference result and therefore underpins the central claim of improved understanding and trust; without these details the evidential support cannot be assessed.
minor comments (1)
  1. The abstract refers to 'experimental and simulation results' without identifying the simulation environment, quantitative performance metrics, or baseline comparisons used to demonstrate effectiveness.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The point raised about the underspecification of the confusion matrix analysis is valid, and we will revise the abstract and relevant sections to provide the necessary details while preserving the core contributions of the work.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the statement that 'experiments were validated through confusion matrix analysis to assess the level of agreement with human expectations' supplies no information on the matrix categories, the mapping from VLM text outputs to discrete classes, the collection of ground-truth labels, or the specific navigation actions or explanation qualities being classified. This analysis is presented as corroborating the user-study preference result and therefore underpins the central claim of improved understanding and trust; without these details the evidential support cannot be assessed.

    Authors: We agree that the abstract statement is too brief and does not supply the requested specifics, which limits evaluation of how the confusion matrix supports the claims of improved understanding and trust. In the revised manuscript we will expand the abstract to concisely describe the matrix categories, the procedure for mapping VLM text outputs to discrete classes, the collection of ground-truth labels, and the navigation actions and explanation qualities evaluated. Corresponding clarifications will also be added to the experimental validation section to make the full methodology transparent and to strengthen the link to the user-study results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical validation relies on external human data

full rationale

The paper describes an empirical system for explainable robot navigation using VLMs and heat maps, with central claims resting on a user study (n=30) measuring preference for real-time explanations and a confusion matrix assessing agreement with human expectations. No equations, parameter fitting, self-citations, or derivation steps appear in the abstract or described content. The validation is framed against independent external human judgments rather than reducing to the system's own outputs by construction, satisfying the criteria for a self-contained empirical result with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work relies on the domain assumption that current vision-language models can reliably interpret social scenes and generate faithful explanations; no free parameters or new invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Vision-language models can accurately perceive and describe social navigation scenarios in a way that matches human expectations.
    The entire explainability module depends on this capability of the underlying VLM.

pith-pipeline@v0.9.0 · 5641 in / 1247 out tokens · 73003 ms · 2026-05-22T20:21:07.408871+00:00 · methodology

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Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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Reference graph

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