点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Artem Voronov等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 一、核心背景与目标 当前移动操作技术正从封闭、结构化的工作单元，向开放、非结构化的大型室内环境拓展——机器人需在陌生杂乱空间中探索，与多样物体及人类互动，同时响 应自然语言指令完成任务（如家庭服务、零售自动化、仓储物流等场景）。但现有方案存在明显瓶颈： 为此，AnywhereVLA提出模块化架构，核心是融合经典导航的鲁棒性与VLA模型的语义理解能力，实现 未知大型室内环境下的语言驱动拾取-放置任务 ，且能在 消费级硬件上实时运行。 二、相关工作回顾：现有方案的优势与不足 1. VLA模型与轻量化优化 2. 扩散Transformer与导航相关方案 三、AnywhereVLA架构：四大核心模块与工作流 AnywhereVLA以自然语言指令为输入，通过四大模块协同输出低级别控制指令（驱动基座车轮与机械臂关节），整体 ...

Core Insights - The article introduces VLN-PE, a physically realistic platform for Vision-Language Navigation (VLN), addressing the gap between simulated models and real-world deployment challenges [3][10][15] - The study highlights the significant performance drop (34%) when transferring existing VLN models from simulation to physical environments, emphasizing the need for improved adaptability [15][30] - The research identifies the impact of various factors such as robot type, environmental conditions, and the use of physical controllers on model performance [15][32][38] Background - VLN has emerged as a critical task in embodied AI, requiring agents to navigate complex environments based on natural language instructions [6][8] - Previous models relied on idealized simulations, which do not account for the physical constraints and challenges faced by real robots [9][10] VLN-PE Platform - VLN-PE is built on GRUTopia, supporting various robot types and integrating high-quality synthetic and 3D rendered environments for comprehensive evaluation [10][13] - The platform allows for seamless integration of new scenes, enhancing the scope of VLN research and assessment [10][14] Experimental Findings - The experiments reveal that existing models show a 34% decrease in success rates when transitioning from simulated to physical environments, indicating a significant gap in performance [15][30] - The study emphasizes the importance of multi-modal robustness, with RGB-D models performing better under low-light conditions compared to RGB-only models [15][38] - The findings suggest that training on diverse datasets can improve the generalization capabilities of VLN models across different environments [29][39] Methodologies - The article evaluates various methodologies, including single-step discrete action classification models and multi-step continuous prediction methods, highlighting the potential of diffusion strategies in VLN [20][21] - The research also explores the effectiveness of map-based zero-shot large language models (LLMs) for navigation tasks, demonstrating their potential in VLN applications [24][25] Performance Metrics - The study employs standard VLN evaluation metrics, including trajectory length, navigation error, success rate, and others, to assess model performance [18][19] - Additional metrics are introduced to account for physical realism, such as fall rate and stuck rate, which are critical for evaluating robot performance in real-world scenarios [18][19] Cross-Embodiment Training - The research indicates that cross-embodiment training can enhance model performance, allowing a unified model to generalize across different robot types [36][39] - The findings suggest that using data from multiple robot types during training leads to improved adaptability and performance in various environments [36][39]