Core Background and Objectives - The current mobile operation technology is expanding from closed, structured work units to open, unstructured large indoor environments, requiring robots to explore unfamiliar and cluttered spaces, interact with diverse objects and humans, and respond to natural language commands for tasks such as home service, retail automation, and warehousing logistics [3] - AnywhereVLA proposes a modular architecture that integrates the robustness of classical navigation with the semantic understanding capabilities of VLA models to achieve language-driven pick-and-place tasks in unknown large indoor environments, capable of real-time operation on consumer-grade hardware [3] Review of Existing Solutions: Advantages and Limitations - VLA models and lightweight optimization strategies are discussed, highlighting their limitations in spatial perception and adaptability to large environments [4] - Existing solutions like MoManipVLA and SmolVLA show performance close to larger models while reducing resource requirements, but they lack spatial awareness for large environments [4] - The limitations of visual-language navigation (VLN) and classical navigation frameworks are outlined, emphasizing the need for improved language understanding and semantic reasoning capabilities [4] AnywhereVLA Architecture: Four Core Modules and Workflow - The AnywhereVLA architecture processes natural language commands through four modules to output low-level control instructions for driving base wheels and robotic arm joints [4] - The workflow includes language instruction parsing, guiding VLA operations, constructing 3D semantic maps, and executing operations based on the identified targets [7] VLA Model Fine-tuning and Hardware Platform - The SmolVLA model is fine-tuned to enhance its operational capabilities, with specific input data and key steps outlined for optimizing performance [13][15] - The HermesBot mobile operation platform is designed specifically for AnywhereVLA, balancing sensing and computational capabilities [16] Experimental Results: Performance and Effectiveness Validation - In an unknown multi-room laboratory setting, 50 pick-and-place tasks were executed, with a core success rate of 46%, and the fine-tuned SmolVLA operation module achieving an 85% success rate [17][22] - The performance metrics for various modules are provided, indicating robust SLAM performance and varying success rates for active environment exploration, navigation, object detection, and VLA manipulation [22] - Time efficiency metrics show that the average task completion time is under 133 seconds for a 5m exploration radius, meeting real-time scene requirements [23]