Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments (2506.14233v1)

Abstract: Large Vision-LLMs (VLMs) have demonstrated potential in enhancing mobile robot navigation in human-centric environments by understanding contextual cues, human intentions, and social dynamics while exhibiting reasoning capabilities. However, their computational complexity and limited sensitivity to continuous numerical data impede real-time performance and precise motion control. To this end, we propose Narrate2Nav, a novel real-time vision-action model that leverages a novel self-supervised learning framework based on the Barlow Twins redundancy reduction loss to embed implicit natural language reasoning, social cues, and human intentions within a visual encoder-enabling reasoning in the model's latent space rather than token space. The model combines RGB inputs, motion commands, and textual signals of scene context during training to bridge from robot observations to low-level motion commands for short-horizon point-goal navigation during deployment. Extensive evaluation of Narrate2Nav across various challenging scenarios in both offline unseen dataset and real-world experiments demonstrates an overall improvement of 52.94 percent and 41.67 percent, respectively, over the next best baseline. Additionally, qualitative comparative analysis of Narrate2Nav's visual encoder attention map against four other baselines demonstrates enhanced attention to navigation-critical scene elements, underscoring its effectiveness in human-centric navigation tasks.

• The paper introduces a vision-action model that embeds implicit language reasoning to convert visual cues and text signals into effective navigation commands.
• The paper proposes a self-supervised learning framework using Barlow Twins loss to focus on critical image regions for enhanced navigation in dynamic settings.
• The paper achieves significant accuracy improvements (up to 52.94% and 41.67% on benchmarks), demonstrating practical impact in human-centric navigation.

Real-Time Visual Navigation with Implicit Language Reasoning

The manuscript titled "Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments" by Payandeh et al. introduces a novel vision-LLM (VLM) framework aimed at enhancing navigation systems in human-centric environments by adopting implicit language reasoning. This approach leverages the predictive capabilities of LLMs to facilitate social navigation, addressing both computational complexity and insensitivity to temporal cues that hitherto plagued previous frameworks.

Overview of Narrate2Nav Framework

Narrate2Nav proposes a real-time vision-action model that intricately embeds language reasoning within its visual encoder, enabling a contextual understanding of human-centered interactions without relying on direct linguistic commands. The strategy involves employing a self-supervised learning framework based on Barlow Twins redundancy reduction loss, which allows the model to internalize natural language reasoning and human intention directly into the encoding process. This is achieved by decoding RGB inputs, motion commands, and textual signals to bridge robot observations with low-level motion instructions vital for point-goal navigation.

Core Contributions

1. Vision-Action Model: The introduction of a real-time vision-action model that transcends visual observation into actionable commands, enriched with human-like language reasoning specifically tuned for dynamic environments.
2. Self-Supervised Learning (SSL) Framework: The authors developed a novel SSL method where textual signals guide the visual encoder to focus on image regions critical for navigation tasks, improving operational efficacy in dynamic settings.
3. Empirical Performance: Extensive evaluation demonstrated significant improvements over current state-of-the-art models, with Narrate2Nav achieving a 52.94% and 41.67% accuracy improvement on benchmark datasets, respectively, emphasizing its practical impact on both accuracy and interpretability.

Quantitative and Qualitative Evaluation

Quantitative assessment focuses on comparing accuracy metrics such as AOE, ADE, and FDE across challenging scenarios compared to established baselines like GNM and ViNT. Narrate2Nav showed superior orientation precision and endpoint prediction accuracy. Qualitative analysis further illustrated enhanced navigation map attention with relevant social scene elements, validating the implicit language reasoning embedded while categorically outperforming similar models.

Implications and Future Directions

The implications of this research are profound:

• Theoretical: The approach advances understanding of vision-LLM application beyond high-level reasoning into embedded language cognition, promising rich avenues for social navigation strategies in robotics.
• Practical: The real-time application ensures practical deployment viability in complex human-centric environments such as crowded public spaces or chaotic event settings.

Future research may address integrating richer forms of linguistic context to enhance spatial reasoning further and adapt the model for varied robot embodiments, ensuring broader applicability across diverse real-world scenarios. Moreover, the scalability of these models in terms of computational efficiency and their adaptability in evolving environments presents promising challenges.

Overall, the paper encapsulates significant strides made toward embedding nuanced language reasoning in vision models to improve the navigational acuity of robotic systems within dynamic human-centric environments, laying foundational work for the next generation of autonomous systems.