点击 最 下方 "在看"和" "并分享，"关注"材料汇 添加 小编微信 ，遇见 志同道合 的你 正文 行业|深度|研究报告 2025年9月 15日 人 形机 器人 行 业 深 度: 驱 动 因素、 现 状 及 趋 势、产业链反相关公司深度梳理 在全球科技革命与产业变革浪潮中,人形机器人正从科幻走向现实,成为继智能手机、新能源汽车后重 塑全球产业格局与推动生产力跃迁的关键赛道。它融合人工智能、机械工程等多领域尖端技术,既肩负 替代人类完成高危、重复、高强度劳动的使命,也是破解全球人口老龄化下劳动力短缺、推动制造业 "柔性智能"升级、重构家庭与公共服务场景的重要突破口。当前,该行业进入"o 到 1"向"1 到 100" 规模化迈进的关键期:政策端中国有顶层设计与地方协同推进:技术端大模型赋予其"通用智能",关 键技术迭代且国产专利、整机企业数量居全球前列:需求端多场景对高效劳动力需求迫切;资本端 2024年国内机器人行业投融资超200亿元,部分城市形成产业集群。不过,行业仍需突破核心零部件 国产替代未完成、量产成本高、复杂场景泛化能力不足等挑战。 目录 一、行业概述 ... ... ... . 二、驱动因素 . ...

Investment Rating - The report emphasizes the importance of 3D data assets and physical simulation engines, indicating a positive outlook on China's physical AI as a scarce asset [3]. Core Insights - The report outlines the five stages of biological intelligence and maps them to embodied intelligence, highlighting that the current missing elements are simulation and planning capabilities [4][10]. - It discusses the evolution of intelligent driving algorithms and their relevance to understanding the development of embodied intelligence models, noting that many core teams in humanoid robotics have extensive experience in the intelligent driving sector [39][41]. - The report identifies the need for physical AI to facilitate real-world interactions for robots, contrasting this with intelligent driving, which inherently avoids physical interactions [4][41]. Summary by Sections 1. Mapping Biological Intelligence to Embodied Intelligence - The report details the five stages of biological intelligence, emphasizing that the current stage of humanoid robots is still early, with a significant gap in simulation learning capabilities [10][35]. - It highlights the importance of understanding the evolutionary history of biological intelligence to inform the development of embodied intelligence [10]. 2. Intelligent Driving and Its Implications - The report reviews the history of intelligent driving algorithms, concluding that the architecture has evolved from 2D images to 3D spatial understanding, which is crucial for developing initial spatial intelligence [39]. - It notes that the transition from traditional algorithms to model-based reinforcement learning is essential for both intelligent driving and humanoid robotics, affecting their usability [39][41]. 3. The Role of Physical AI - The report emphasizes that physical AI is critical for enabling robots to interact with the physical world, addressing the challenges of data scarcity in the robotics industry [4][10]. - It contrasts the requirements for physical interaction in humanoid robots with the goals of intelligent driving, which focuses on avoiding physical collisions [41].