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].

具身智能，作为近年来人工智能领域的热点之一，成为产业界和学术界重点关注的方向。特别是在人形机器人这个载体上，它所承载的感知、运 动、决策等能力，让具身智能从概念逐渐走向落地。但与此同时，也有不少值得深入探讨的问题浮出水面：为什么具身智能的发展似乎格外偏 爱"人形"？是否只有模仿人类形态，才是实现智能的最佳路径？在面对数据、算力、模型架构等现实挑战时，我们究竟处于怎样的阶段？距离真 正的通用机器人，还有多少"里程"要走？ 基于此，CSDN《万有引力》栏目特别策划了一期以"十问具身智能：我们离通用机器人还有多远？"为主题的深度对话，邀请了北京邮电大学人 工智能学院副教授陈光@爱可可-爱生活、深圳市人工智能与机器人研究院副研究员夏轩、Roboraction.AI 首席执行官黄浴，在栏目主理人 CSDN &《新程序员》执行总编唐小引主持下，三位专家将从技术演进、研究现状、产业应用等多个角度切入，带大家一同拆解具身智能面临的"关键问 题"，看清这条通往未来机器人的发展路径。 夏轩：在专业背景方面，我早期的研究主要集中于计算机视觉领域（CV），涵盖无人机图像处理、工业图像处理以及生成模型等方向。在扩散模 型兴起之前，我也 ...