Core Viewpoint - Embodied intelligence is a key focus in the AI field, particularly in humanoid robots, raising questions about the best path to achieve true intelligence and the current challenges in data, computing power, and model architecture [2][5][36]. Group 1: Development Stages of Embodied Intelligence - The industry anticipates 2025 as a potential "year of embodied intelligence," with significant competition in multimodal and embodied intelligence sectors [5]. - NVIDIA's CEO Jensen Huang announced the arrival of the "general robot era," outlining four stages of AI development: Perception AI, Generative AI, Agentic AI, and Physical AI [5][36]. - Experts believe that while progress has been made, the journey towards true general intelligence is still ongoing, with many technical and practical challenges remaining [36][38]. Group 2: Transition from Autonomous Driving to Embodied Intelligence - Many researchers from the autonomous driving sector are transitioning to embodied intelligence due to the overlapping technologies and skills required [17][22]. - Autonomous driving is viewed as a specific application of robotics, focusing on perception, planning, and control, but lacks the interactive capabilities needed for general robots [17][19]. - The integration of expertise from autonomous driving is seen as a bridge to advance embodied intelligence, enhancing technology fusion and development [18][22]. Group 3: Key Challenges in Embodied Intelligence - Current robots often lack essential capabilities, such as tactile perception, which limits their ability to maintain balance and perform complex tasks [38][39]. - The operational capabilities of many humanoid robots are still in the demonstration phase, lacking the ability to perform tasks in real-world contexts [38][39]. - The complexity of high-dimensional systems poses significant challenges for algorithm robustness, especially as more sensory channels are integrated [39]. Group 4: Future Applications and Market Focus - The focus for developers should be on specific application scenarios rather than pursuing general capabilities, with potential areas including home care and household services [48]. - Industrial applications are highlighted as promising due to their scalability and the potential for replicable solutions once initial systems are validated [48]. - The gap between laboratory performance and real-world application remains significant, necessitating a focus on improving system accuracy in specific contexts [46][47].

该工作由通用人工智能研究院 × 北京大学联手打造。第一作者郑欣悦为通用人工智能研究院研究员，共同一作为北京大学人工智能研究院博士生林昊苇， 通讯作者为北京大学助理教授梁一韬和通用人工智能研究院研究员郑子隆。 开发能在开放世界中完成多样任务的通用智能体，是 AI 领域的核心挑战。开放世界强调环境的动态性及任务的非预设性，智能体必须具备真正的泛化能力 才能稳健应对。然而，现有评测体系多受限于任务多样化不足、任务数量有限以及环境单一等因素，难以准确衡量智能体是否真正 「 理解 」 任务，或仅是 「 记住 」 了特定解法。 为此，我们构建了 Minecraft Universe （ MCU ） —— 一个面向通用智能体评测的生成式开放世界平台。 MCU 支持自动生成无限多样的任务配置，覆 盖丰富生态系统、复杂任务目标、天气变化等多种环境变量，旨在全面评估智能体的真实能力与泛化水平。该平台基于高效且功能全面的开发工具 MineStudio 构建，支持灵活定制环境设定，大规模数据集处理，并内置 VPTs 、 STEVE-1 等主流 Minecraft 智能体模型，显著简化评测流程，助力智 能体的快速迭代与发展。 开放世界 ...