具身智能大脑+首个SaaS开源框架，智源研究院刷新10项测评基准，加速群体智能新范式

Core Insights - The article discusses the advancements in embodied intelligence through the launch of RoboBrain 2.0 and RoboOS 2.0, which aim to enhance robotic capabilities in real-world environments [1][3][25]. Group 1: RoboBrain 2.0 Features - RoboBrain 2.0 integrates perception, reasoning, and planning, addressing three core limitations of current AI models: spatial understanding, temporal modeling, and long-chain reasoning [5][8]. - The model employs a modular encoder-decoder architecture, enabling it to process high-resolution images, multi-view inputs, video frames, language instructions, and scene graphs as a unified multimodal sequence [8][10]. - It has demonstrated superior performance in spatial reasoning benchmarks, achieving state-of-the-art results in various tests, including BLINK and CV-Bench [22][23]. Group 2: Training Methodology - The training of RoboBrain 2.0 is structured in three progressive phases, focusing on foundational spatiotemporal learning, embodied spatiotemporal enhancement, and chain-of-thought reasoning in embodied contexts [14][16][18]. - The model utilizes a diverse multimodal dataset, which includes high-resolution images, multi-view video sequences, and complex natural language instructions, to enhance its capabilities in embodied environments [11][19]. Group 3: RoboOS 2.0 Framework - RoboOS 2.0 is the world's first embodied intelligence SaaS platform that supports serverless, lightweight deployment of robotic bodies, facilitating multi-agent collaboration [27][28]. - The framework features a cloud-based brain model for high-level cognition and distributed modules for specialized skill execution, enhancing real-time environmental awareness [28][30]. - It has achieved a 30% overall performance improvement and reduced average response latency to below 3ms, significantly enhancing communication efficiency [29]. Group 4: Application and Deployment - RoboBrain 2.0 and RoboOS 2.0 are fully open-sourced, providing model weights, training code, and evaluation benchmarks to developers [32]. - The systems are designed for various deployment scenarios, including commercial kitchens and home environments, enabling robots to perform complex tasks collaboratively [25][31].