今年下半年以来，具身智能机器人行业连续宣布亿元级商业化大单，营造出一派乐观的落地前景。但也 有从业者直言，看不懂一些订单背后的虚实。 在11月20日的智源研究院"具身开放日"上，原力灵机创始人兼CEO唐文斌抛出诸多疑问："这些订单它 到底解决了什么问题？真的（商业）闭环了吗？它创造的场景价值是真实的吗？"原力灵机是一家成立 于2025年3月的具身智能初创公司，11月中旬刚完成由阿里巴巴领投的数亿元A+轮融资。 王仲远建议，政府层面应更多从政策上给予支持与引导，避免直接提需求，因为真正的需求始终来自企 业和用户侧。 具身智能模型"难产"背后，数据短缺是一个老生常谈的问题。业内为此爆发了至今仍在持续的真机数据 与仿真数据路线之争。 智源研究院的具身训练场。图：智源研究院 尽管技术仍不成熟，但具身智能公司在今年纷纷发力商业化布局。这背后，既有投资人对创业公司"造 血"能力或跑通商业闭环能力的考验压力，同时也源于机器人企业在真实场景中发现问题、迭代产品的 现实需求。从应用场景来看，众多公司集中涌入工业和物流领域的搬运、分拣、安防，以及商用领域的 导览、导购和文娱表演等方向。 机器人能力的有限性，也在李凯的预期之中。作 ...

Core Insights - The event highlighted the latest advancements in embodied intelligence by the Zhiyuan Research Institute, focusing on the importance of world models and the development of a comprehensive embodied brain system [2][3] Group 1: Zhiyuan's Full-Stack Layout - Zhiyuan introduced the native multimodal world model Emu3.5, which expanded training data from 15 years of video to 790 years and increased parameter size from 8 billion to 34 billion, enhancing video and image generation speed [5] - The institute is constructing a cross-heterogeneous ontology embodied intelligence system, including RoboBrain, RoboOS, and RoboBrain-0, deployed across various robotic forms for tasks ranging from navigation to complex interactions [5] Group 2: Key Elements of Embodied Intelligence - The role of world models in embodied intelligence was debated, with experts emphasizing the need for models that predict the next state based on the robot's form and goals, rather than merely generating videos [7][10] - There is a consensus that embodied intelligence should not follow the current language-first paradigm but rather adopt a structure centered on action and perception [10][12] - The importance of real data was highlighted, with discussions on the necessity of combining real, simulated, and video data for effective learning in robots [15][17] Group 3: Investment Priorities - When asked how to allocate 10 billion, experts prioritized talent acquisition, computational power, and data engines as key investment areas [19][21] - There were differing views on the importance of infrastructure versus model development, with some advocating for a focus on creating a comprehensive data engine for continuous digitalization [21][22] Group 4: Human-like Robots and Hardware Limitations - The debate on whether human-like robots represent the ultimate form of embodied intelligence concluded that neither models nor hardware define each other; rather, the specific application scenarios dictate the requirements [22][24] - Experts suggested that a layered structure for embodied intelligence should be adopted, where higher-level models can be reused across different robotic forms, but lower-level models must be tailored to specific hardware [23][24] Conclusion - The discussions at the event signaled a proactive search for solutions to achieve a closed-loop system in embodied intelligence, emphasizing the need for models, hardware, and scaling to evolve together [24]

Core Insights - The topic of embodied intelligence is a major focus at the 2025 Zhongguancun Forum, with the introduction of the RoboOS framework and the open-source RoboBrain model [1][3] - Multi-modal large model technology is expected to enhance the intelligence of robots, allowing them to better understand and interact with the physical world [2][3] Group 1: Multi-modal Large Models - Multi-modal large models enable AI to perceive and understand the world through various data types, such as medical imaging and sensor data, facilitating the transition from digital to physical environments [2] - The performance improvement of large language models has slowed due to the exhaustion of available internet text data, necessitating the integration of multi-modal capabilities [2] Group 2: RoboBrain and RoboOS - RoboBrain and RoboOS are designed to support cross-scenario, multi-task deployment and collaboration among different types of robots, enhancing their general intelligence [3] - RoboBrain can interpret human commands and visual inputs to generate actionable plans based on real-time feedback, supporting various robotic configurations [3] Group 3: Industry Development and Challenges - The open-source approach is seen as a key driver for rapid development in the AI industry, allowing for collaboration among hardware, model, and application vendors [4] - Despite the potential of humanoid robots, there are significant challenges in their industrial application, with many still in the early stages of development [5] - The realization of Artificial General Intelligence (AGI) is projected to take an additional 5-10 years, influenced by advancements in embodiment capabilities and data accumulation [5]