Core Insights - MAESTRO is a modular robotic framework centered around Vision Language Models (VLM), achieving zero-shot operational performance without extensive training data, while offering scalability and debuggability [2][5][22] Group 1: Innovation and Design - Current mainstream robotics development relies on large-scale "observation-action" datasets, which are costly and limited, hindering progress [4] - MAESTRO adopts a differentiated approach, utilizing VLM to avoid dependency on robot-specific data and integrating mature specialized tools for enhanced low-level operations [6][5] - The framework employs a closed-loop interaction mechanism, continuously monitoring environmental feedback to adjust actions in real-time, forming an adaptive cycle of perception, action, and learning [5][6] Group 2: Core Module Toolset - The modular design adheres to six principles, addressing diverse robotic operational needs, including perception, control, and geometry [8] - Key modules include: - Perception: Enhances visual information accuracy through a hierarchical approach [10] - Control: Integrates Cartesian control and collision-free motion planning for safety [10] - Geometry & Linear Algebra: Provides tools for spatial reasoning [10] - Image Editing: Improves visual grounding capabilities [10] - Mobile Operation Extensions: Adapts to mobile robot scenarios with navigation and active perception tools [10] Group 3: Evolution Mechanism - MAESTRO records past task execution codes and outcomes to provide contextual examples for VLM, optimizing code generation and enhancing performance after minimal real-world trials [12] Group 4: Experimental Results and Performance Analysis - MAESTRO demonstrated superior performance in desktop operations, significantly outperforming existing VLA models in six out of seven tasks, particularly in semantic reasoning and long-term memory tasks [17] - In mobile operations, MAESTRO achieved high completion rates, with specific tasks scoring 96.0±8.9 and 93.3±14.9 [17] - The evolution capability was highlighted by improving task completion from 35% to 85.0±7.4 after three iterations in a door-opening task [17] Group 5: Key Module Ablation Analysis - Removing advanced perception modules drastically reduced task completion rates, indicating the importance of precise perception for complex operations [20] - The absence of geometry modules also negatively impacted performance, underscoring the necessity of spatial reasoning tools [20] Group 6: Future Directions - MAESTRO's framework is positioned as an effective alternative to large-scale robotic training paths, with future enhancements aimed at optimizing VLM inference speed, improving low-level control capabilities, and increasing reasoning stability in complex scenarios [22]