点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Junyao Shi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。 MAESTRO 是一种以视觉语言模型（VLM）为核心的模块化机器人框架，通过动态组合感知、规划、控制等专用模块，在无需大规模机器人训练数据的情况 下，实现了超越现有视觉语言动作（VLA）模型的零样本操作性能，同时具备可扩展性、可调试性等优势。 论文链接：https://arxiv.org/pdf/2511.00917 核心架构与关键设计 1. 整体框架 MAESTRO 以VLM编码代理为核心，接收语言指令和场景图像后，动态编写代码组合工具模块，形成程序化策略。框架采用闭环交互机制，在执行过程中持续 监控环境反馈，实时调整代码和动作，构成"感知-动作-学习"的自适应循环。 利用VLM已有的强大通用能力，避免对机器人专属数据的依赖； 通过模块化设计整合机器人领域成熟的专用工具，弥补VLM在低级别操作上的不足； 突破传统模 ...

Core Insights - The article discusses the launch of RoboChallenge, a large-scale, multi-task benchmark testing platform for embodied intelligence, initiated by Dexmal and Hugging Face, aimed at addressing the lack of real machine testing in the field [5][41]. Group 1: Challenges in the Embodied Intelligence Field - The embodied intelligence sector has seen rapid advancements, but the absence of real machine testing and limitations of existing evaluation systems have become significant bottlenecks [3][4]. - Current mainstream benchmarks primarily rely on simulation environments, leading to issues where algorithms that perform well in simulations fail in real-world applications [4][10]. Group 2: Introduction of RoboChallenge - RoboChallenge is the first large-scale benchmark testing platform that allows real robots to perform tasks in a physical environment, providing a more reliable and comparable evaluation standard for visual language action models (VLAs) [5][10]. - The platform aims to overcome challenges related to performance validation in real environments, standardized testing conditions, and accessibility [5][10]. Group 3: Features of RoboChallenge - RoboChallenge features a "remote robot" paradigm, allowing users to interact with real machines without needing hardware, thus lowering the entry barrier for researchers and developers [15][19]. - The platform supports a wide range of tasks, with an initial benchmark set (Table30) comprising 30 diverse tasks designed to evaluate core capabilities of VLA models [12][26]. Group 4: Evaluation Mechanism - The evaluation mechanism combines end-to-end task success rates with process scoring, ensuring a rigorous and transparent assessment of models [16][20]. - RoboChallenge employs a "visual input matching" method to ensure consistency in testing conditions, reducing variability caused by human testers [23][25]. Group 5: Open and Collaborative Ecosystem - RoboChallenge promotes an open ecosystem by providing free access to evaluation services, publicly sharing task demonstration data, and ensuring transparency in results [34][41]. - The platform encourages collaboration among researchers, developers, and industry professionals, fostering innovation in the field of embodied intelligence [38][41]. Group 6: Future Directions - RoboChallenge plans to expand its capabilities by introducing more robot types and challenging tasks, aiming to enhance the evaluation of embodied intelligence in real-world scenarios [42].

Core Viewpoint - Physical Intelligence (PI) is advancing the development of general-purpose robots by enhancing their capabilities through the introduction of the Visual-Language-Action (VLA) model, which integrates visual perception and action generation for robots in open environments [2][6][12]. Group 1: VLA and Its Development - VLA is an application of Visual-Language Models (VLM) in robotics, enabling robots to understand and generate action commands based on visual and textual inputs [6][12]. - The PI team has built a comprehensive data engine from scratch, emphasizing the importance of data diversity in improving robot generalization [3][31]. - The introduction of the "Knowledge Insulation" mechanism aims to address the limitations of traditional model training by restructuring the training process [3][47]. Group 2: Challenges in Open World Deployment - The three main challenges in deploying robots in open environments are data gaps, performance instability, and the complexity of hardware platform migration [3][54]. - Data scarcity in robotics is a significant issue, as the required interaction data is not as readily available as textual data on the internet [54]. - Performance stability remains a challenge, with current models being more demonstration-ready than deployment-ready, necessitating further algorithmic breakthroughs [54][56]. Group 3: Future Directions and Innovations - PI aims to create a universal and customizable robotic intelligence ecosystem, allowing various robots to perform diverse tasks through natural language commands [61][62]. - The company is exploring the concept of "Robot Model as a Service" (RMaaS), which would provide tailored robotic solutions through cloud and local deployment [62]. - The focus for the next 1-2 years will be on overcoming performance bottlenecks and developing standardized evaluation systems to ensure reliable model performance across different environments [60][61].