mlsanityData to 14 Oct 2026

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

arXiv 2504.05477 ↗First posted 7 Apr 2025cs.ROcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 11 of its 24 references that are in the map. First posted on arXiv 7 Apr 2025.

Builds on

11 of 24 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models19 Dec
  2. 2024Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interaction15 Apr
  3. 2024VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models30 Mar
  4. 2023VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation6 Dec

Other references · 7

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  2. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  3. 2021Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recoverycited by 32
  4. 2024Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spacescited by 17
  5. 2022Explainable Reinforcement Learning via Model Transformscited by 11
  6. 2022Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarioscited by 5
  7. 2023A Surrogate Model Framework for Explainable Autonomous Behaviourcited by 4

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  1. 2026Vision-Language Models for Deployable Social Robot Navigation: Bridging Semantic Reasoning and Low-Level Control
  2. 2026G2-Nav: Grounded and Guarded Vision-Language Costmaps for Robot Social Navigation ↗
  3. 2025Towards Multimodal Social Conversations with Robots: Using Vision-Language Models
  4. 2026Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
  5. 2026Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies
  6. 2024Explaining Autonomy: Enhancing Human-Robot Interaction through Explanation Generation with Large Language Models
  7. 2025ViLAM: Distilling Vision-Language Reasoning into Attention Maps for Social Robot Navigation
  8. 2025SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation