EvolveNav: Self-Improving Embodied Reasoning for LLM-Based Vision-Language Navigation

• URL: http://arxiv.org/abs/2506.01551v1
• Date: Mon, 02 Jun 2025 11:28:32 GMT
• Title: EvolveNav: Self-Improving Embodied Reasoning for LLM-Based Vision-Language Navigation
• Authors: Bingqian Lin, Yunshuang Nie, Khun Loun Zai, Ziming Wei, Mingfei Han, Rongtao Xu, Minzhe Niu, Jianhua Han, Liang Lin, Cewu Lu, Xiaodan Liang,
• Abstract summary: We propose a novel sElf-improving embodied reasoning framework for boosting Vision-Language Navigation, dubbed EvolveNav.<n>Our EvolveNav consists of two stages: (1) Formalized CoT Supervised Fine-Tuning, where we train the model with formalized CoT labels to activate the model's navigational reasoning capabilities and increase the reasoning speed; (2) Self-Reflective Post-Training, where the model is iteratively trained with its own reasoning outputs as self-enriched CoT labels to enhance the supervision diversity.
• Score: 111.0993686148283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building Vision-Language Navigation (VLN) agents which can navigate following natural language instructions is a long-standing goal in human-robot interaction applications. Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for improving navigation, and simultaneously mitigate the domain gap between LLMs' training corpus and the VLN task. However, these approaches primarily adopt direct input-output mapping paradigms, causing the mapping learning difficult and the navigational decisions unexplainable. Chain-of-Thought (CoT) training is a promising way to improve both navigational decision accuracy and interpretability, while the complexity of the navigation task makes the perfect CoT labels unavailable and may lead to overfitting through pure CoT supervised fine-tuning. In this paper, we propose a novel sElf-improving embodied reasoning framework for boosting LLM-based vision-language Navigation, dubbed EvolveNav. Our EvolveNav consists of two stages: (1) Formalized CoT Supervised Fine-Tuning, where we train the model with formalized CoT labels to both activate the model's navigational reasoning capabilities and increase the reasoning speed; (2) Self-Reflective Post-Training, where the model is iteratively trained with its own reasoning outputs as self-enriched CoT labels to enhance the supervision diversity. A self-reflective auxiliary task is also introduced to encourage learning correct reasoning patterns by contrasting with wrong ones. Experimental results on the popular VLN benchmarks demonstrate the superiority of EvolveNav over previous LLM-based VLN approaches. Code is available at https://github.com/expectorlin/EvolveNav.

Related papers

• VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning [77.34267241692706]
  Vision-Language Navigation (VLN) is a core challenge in embodied AI, requiring agents to navigate real-world environments using natural language instructions.<n>We propose VLN-R1, an end-to-end framework that leverages Large Vision-Language Models (LVLM) to directly translate egocentric video streams into continuous navigation actions.
  arXiv  Detail & Related papers  (2025-06-20T17:59:59Z)
• NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models [30.685419129265252]
  We bridge the divide between VLN-specialized models and LLM-based navigation paradigms. We exploit a way to incorporate LLMs and navigation policy networks for effective action predictions and navigational reasoning.
  arXiv  Detail & Related papers  (2024-07-17T07:44:26Z)
• TINA: Think, Interaction, and Action Framework for Zero-Shot Vision Language Navigation [11.591176410027224]
  This paper presents a Vision-Language Navigation (VLN) agent based on Large Language Models (LLMs) We propose the Thinking, Interacting, and Action framework to compensate for the shortcomings of LLMs in environmental perception. Our approach also outperformed some supervised learning-based methods, highlighting its efficacy in zero-shot navigation.
  arXiv  Detail & Related papers  (2024-03-13T05:22:39Z)
• NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning [97.88246428240872]
  Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions.<n>Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability.<n>This paper introduces a novel strategy called Navigational Chain-of-Thought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision.
  arXiv  Detail & Related papers  (2024-03-12T07:27:02Z)
• NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models [17.495162643127003]
  We introduce the NavGPT to reveal the reasoning capability of GPT models in complex embodied scenes. NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status. We show that NavGPT is capable of generating high-quality navigational instructions from observations and actions along a path.
  arXiv  Detail & Related papers  (2023-05-26T14:41:06Z)
• BEVBert: Multimodal Map Pre-training for Language-guided Navigation [75.23388288113817]
  We propose a new map-based pre-training paradigm that is spatial-aware for use in vision-and-language navigation (VLN) We build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map. Based on the hybrid map, we devise a pre-training framework to learn a multimodal map representation, which enhances spatial-aware cross-modal reasoning thereby facilitating the language-guided navigation goal.
  arXiv  Detail & Related papers  (2022-12-08T16:27:54Z)
• Cross-modal Map Learning for Vision and Language Navigation [82.04247028482244]
  We consider the problem of Vision-and-Language Navigation (VLN) In contrast to other works, our key insight is that the association between language and vision is stronger when it occurs in explicit spatial representations. We propose a cross-modal map learning model for vision-and-language navigation that first learns to predict the top-down semantics on an egocentric map for both observed and unobserved regions.
  arXiv  Detail & Related papers  (2022-03-10T03:30:12Z)
• Contrastive Instruction-Trajectory Learning for Vision-Language Navigation [66.16980504844233]
  A vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works fail to discriminate the similarities and discrepancies across instruction-trajectory pairs and ignore the temporal continuity of sub-instructions. We propose a Contrastive Instruction-Trajectory Learning framework that explores invariance across similar data samples and variance across different ones to learn distinctive representations for robust navigation.
  arXiv  Detail & Related papers  (2021-12-08T06:32:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.