NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models (2305.16986v3)

Abstract: Trained with an unprecedented scale of data, LLMs like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In this work, we introduce the NavGPT, a purely LLM-based instruction-following navigation agent, to reveal the reasoning capability of GPT models in complex embodied scenes by performing zero-shot sequential action prediction for vision-and-language navigation (VLN). At each step, NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status, and makes the decision to approach the target. Through comprehensive experiments, we demonstrate NavGPT can explicitly perform high-level planning for navigation, including decomposing instruction into sub-goal, integrating commonsense knowledge relevant to navigation task resolution, identifying landmarks from observed scenes, tracking navigation progress, and adapting to exceptions with plan adjustment. Furthermore, we show that LLMs is capable of generating high-quality navigational instructions from observations and actions along a path, as well as drawing accurate top-down metric trajectory given the agent's navigation history. Despite the performance of using NavGPT to zero-shot R2R tasks still falling short of trained models, we suggest adapting multi-modality inputs for LLMs to use as visual navigation agents and applying the explicit reasoning of LLMs to benefit learning-based models.

Overview of "NavGPT: Explicit Reasoning in Vision-and-Language Navigation with LLMs"

The paper "NavGPT: Explicit Reasoning in Vision-and-Language Navigation with LLMs" introduces an innovative navigation agent, NavGPT, that leverages LLMs specifically designed for zero-shot vision-and-language navigation tasks. The authors present a compelling case for integrating LLMs into embodied navigation tasks, utilizing models like GPT-4 to decode complex navigation scenarios. NavGPT distinctly exploits LLMs' reasoning capabilities to perform sequential action predictions and high-level planning in vision-and-language navigation without prior task-specific training.

Key Contributions

1. Pure LLM-based Navigation Agent: NavGPT is engineered as a purely LLM-based agent for instruction-following navigation tasks. Without learning from task-specific data, it performs zero-shot sequential action prediction by parsing textual descriptions of visual observations, navigation history, and potential future directions.
2. Explicit Reasoning Capabilities: Through NavGPT, the authors unveil the reasoning prowess of LLMs, demonstrating performance qualities such as instruction decomposition into sub-goals, integration of commonsense knowledge, and identification of landmarks from observed scenes. These capabilities are accessed and explicated via an explicit reasoning trace, bridging the agent's decision-making process and its action space.
3. Combination with Visual Foundation Models (VFMs): To translate visual scenes into natural language descriptions, NavGPT collaborates with VFMs. This feature enhances its capability to interpret complex environments and respond to linguistic instructions effectively.
4. Navigation Planning and History Awareness: NavGPT showcases impressive self-monitoring abilities, which include tracking navigation progress and dynamically adjusting plans based on exceptions encountered in the navigation task. Additionally, GPT-4’s application in reimagining navigation instructions and visualizing top-down trajectory maps highlights LLMs' potential in spatial understanding and historical awareness.

Results and Implications

Despite the NavGPT's zero-shot navigation performance not reaching the levels of fine-tuned models, it provides a noteworthy baseline when operating in unseen environments. The insights derived propose significant implications for future direction:

• Integration of Multi-modal Inputs: The authors suggest enhancing LLMs with multi-modality inputs to circumvent the limitations observed in current implementations, primarily due to information loss during visual-to-text conversion.
• Enhancing Linguistic Descriptions: Advancements in dynamically generating language descriptions of visual scenes can extend the applications beyond static translations, permitting more robust real-time interactions.
• Utilization of High-level Reasoning and Planning: The paper encourages the exploration of LLMs’ reasoning insights to design learning-based navigation models, which could lead to more transparent and explainable autonomous systems.

Future Directions

The research emphasizes the potential pathways for improving NavGPT-type systems by advancing methodologies to combine LLMs with upstream modality systems for more intuitive embodied agents. This includes developing LLMs that can inherently process visual information or embarking on hybrid systems that knit together LLMs’ reasoning with other specialized models. Furthermore, the paper advocates for continuous exploration into the efficiency of dynamic prompting strategies to harness the maximum potential of LLMs in real-world navigation tasks.

In conclusion, "NavGPT: Explicit Reasoning in Vision-and-Language Navigation with LLMs" sets forth a foundational step in leveraging LLMs for vision-and-language navigation tasks, with ramifications for creating more comprehensive and intelligent navigation models. The paper opens up novel avenues for research at the intersection of language understanding, robotics, and scene interpretation, paving the way for future innovations in artificial intelligence.