WMNav：将视觉-语言模型集成到世界模型中用于物体目标导航
WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation
AI总结：
WMNav提出一种由视觉-语言模型驱动的世界模型导航框架，通过预测决策结果、维护好奇心价值图并采用两阶段动作提议策略，在HM3D和MP3D上超越现有零样本基准。
摘要：
物体目标导航——要求智能体在未见过的环境中定位特定物体——仍然是具身人工智能中的一个核心挑战。尽管基于视觉-语言模型（VLM）的智能体近期通过提示展现了有前景的感知和决策能力，但尚未有任何工作建立起一种完全模块化的世界模型设计，能够通过预测世界的未来状态来减少与环境之间高风险、高成本的交互。我们提出了WMNav，一种由视觉-语言模型（VLM）驱动的新型基于世界模型的导航框架。它预测决策的可能结果并构建记忆，为策略模块提供反馈。为了保留环境的预测状态，WMNav提出了在线维护的好奇心价值图作为世界模型记忆的一部分，为导航策略提供动态配置。通过按照类人思维过程进行分解，WMNav基于世界模型规划与观测之间的反馈差异做出决策，有效缓解了模型幻觉的影响。为了进一步提升效率，我们实现了一种两阶段动作提议策略：先进行广泛探索，再进行精确定位。在HM3D和MP3D上的广泛评估验证了WMNav在成功率和探索效率上均超越了现有的零样本基准（绝对提升：在HM3D上SR提升+3.2%、SPL提升+3.2%，在MP3D上SR提升+13.5%、SPL提升+1.1%）。项目页面：https://b0b8k1ng.github.io/WMNav/。
英文摘要：
Object Goal Navigation-requiring an agent to locate a specific object in an unseen environment-remains a core challenge in embodied AI. Although recent progress in Vision-Language Model (VLM)-based agents has demonstrated promising perception and decision-making abilities through prompting, none has yet established a fully modular world model design that reduces risky and costly interactions with the environment by predicting the future state of the world. We introduce WMNav, a novel World Model-based Navigation framework powered by Vision-Language Models (VLMs). It predicts possible outcomes of decisions and builds memories to provide feedback to the policy module. To retain the predicted state of the environment, WMNav proposes the online maintained Curiosity Value Map as part of the world model memory to provide dynamic configuration for navigation policy. By decomposing according to a human-like thinking process, WMNav effectively alleviates the impact of model hallucination by making decisions based on the feedback difference between the world model plan and observation. To further boost efficiency, we implement a two-stage action proposer strategy: broad exploration followed by precise localization. Extensive evaluation on HM3D and MP3D validates WMNav surpasses existing zero-shot benchmarks in both success rate and exploration efficiency (absolute improvement: +3.2% SR and +3.2% SPL on HM3D, +13.5% SR and +1.1% SPL on MP3D). Project page: https://b0b8k1ng.github.io/WMNav/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
