人机协作

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越疆(02432.HK):深耕人机协作 探索具身智能
Ge Long Hui· 2025-08-08 02:40
机构:中金公司 潜在催化剂:新产品开拓超预期;人形机器人销量超预期等 盈利预测与估值 风险 研究员:郭威秀/张怡康/刘中玉/彭虎/贾顺鹤/王梓琳 投资亮点 首次覆盖越疆(02432)给予跑赢行业评级,目标价61.00 港元。越疆深耕协作机器人,并在新品类延 展。我们基于中期P/S 估值法,理由如下: 全球协作机器人创新者,成长潜力突出。越疆2015 年成立,2021-2024年营收复合增速29%。公司主营 涵盖四轴、六轴、复合协作机器人,已有超过80 家全球500 强客户。根据灼识咨询,2023 年越疆在全 球协作机器人行业中排名前二(市占率13%,按照出货量)。协作机器人基于人机交互的优势方兴未 艾,根据MIR 预测,2028 年全球协作机器人销量有望达到20 万台,隐含2024-2028E 复合增速为30%。 国际化业务布局,多元场景并举。1)2024 年公司境外销售占比为54%,公司在美国、德国以及日本等 国家设有分支机构,依托山东生产基地,布点全球销售网络。2)公司产品覆盖工业、商业、教育等多 元化场景,自研电机和编码器技术、驱控及一体化关节技术。 积极布局具身智能,拓宽产品边界。公司AI 赋能平台X ...
人类还能守住哪张工位?
3 6 Ke· 2025-08-05 02:41
看了份报告,时间不算太新。 2025年7月10日,来自于微软研究院(Microsoft Research),名字叫:《Working with AI: Measuring the Occupational Implications of Generative AI》。 中文可以理解为《生成式AI与就业:职业影响评估》 整个报告,讨论生成式AI对全球职场的具体影响,它们通过分析20万个匿名对话,揭露AI如何改变我 们的生活方式,并评估了不同职业受AI影响的程度。 大概看完后,我认为可以分五个部分。第一个部分,主要讨论:生成式AI如何通过任务分类影响不同 职业的适用性。 01 这个分数综合了三个维度,分别是: 覆盖率,这个职业的多少任务能被AI覆盖?完成率,AI在这些任务上能做到什么程度?影 响范围,这些任务在整个工作中的权重有多大? 最终结果显示,越依赖信息处理、文本生成、沟通协调的职业,AI适用性越高;越是依赖物理操作、 现场判断、人际互动的,AI越难插手。 像销售这个职业,AI适用性评分高达0.84。 他们调研很有趣,用美国劳工部O*NET职业分类体系,把工作分拆到任务层面。 最上层叫广义工作活动,比如:提 ...
MIT研发「三合一」机器人训练神器:零基础用户也能教机器人学技能
机器人圈· 2025-08-04 11:38
以下文章来源于机器人技术与应用 ,作者唐 机器人技术与应用 . 传播企业信息和市场行情,交流业内创新成果,推动行业技术进步。宣传报道国内外机器人技术领域最新技术、 成果和信息,促进企业转型升级,搭建产学研交流平台。 机器人,正在 "听懂" 人类。 从需要写代码到 "说句话、动动手" 就能教,机器人训练方式的变化,其实是 "人机协作" 的一大步。 1. 远程操控模式 : 通过操纵杆控制机器人完成高危任务(如处理有毒物质); 长期以来,训练机器人掌握新技能是编程专家的专属领域,但 MIT工程师最新研发的 多功能演示界面( VDI) 将 彻底改写这一规则。这款手持式智能工具首次整合 远程操控、运动引导、自然模仿 三大训练模式于一体,让普通 工人、护理人员甚至家庭成员都能成为机器人的 "老师"。 简单说,它是一个装了摄像头、力传感器和标记点的 "万能手柄",插在任何常见协作机械臂上即可使用。 这种方式的创新点: | 传统做法 | 新做法 (VDI) | | --- | --- | | 写程序 → 调试 → 再写程序 | 直接上手演示 | | 一种训练方法用到黑 | 3 种方法随时切换 | | 只有工程师能操作 | 工 ...
AI迁徙一代:跨越技术断层的中坚力量
腾讯研究院· 2025-08-01 08:33
Core Viewpoint - The article discusses the emergence of the "AI Migrant" generation, a group that navigates the complexities of life in an AI-dominated world, experiencing both disconnection and adaptation as they transition from pre-AI to post-AI realities [4][12]. Group 1: AI's Impact on Work and Education - AI is reshaping the nature of work, creating new job types while eliminating traditional roles, as highlighted in the World Economic Forum's 2023 report [4][17]. - The "AI Migrant" generation has experienced a significant shift in education from standardized teaching to personalized learning, influenced by AI technologies [7][16]. - The skills required in the workforce are evolving rapidly, with the skill update cycle shrinking from ten years to as short as three years, necessitating continuous learning and adaptation [18][19]. Group 2: Social and Cultural Dynamics - The distribution of the "AI Migrant" generation is uneven across urban and rural areas, with varying levels of AI penetration affecting their experiences [5][13]. - This generation embodies a mix of passive migration and active adaptation, reflecting a blend of old and new identities shaped by technological advancements [12][20]. - The cultural identity of the "AI Migrant" generation is characterized by a unique subculture that values efficiency, innovation, and freedom, while also facing challenges like anxiety and burnout [13][24]. Group 3: Ethical Considerations and Responsibilities - The "AI Migrant" generation is increasingly aware of ethical issues surrounding AI, such as algorithmic bias and data privacy, and they advocate for responsible AI development [21][23]. - Their ethical awakening emphasizes the importance of individual rights and the need for diverse perspectives in technology development to ensure fairness and inclusivity [22][23]. - The generation's commitment to ethical practices reflects a broader responsibility towards society and future generations, as they navigate the complexities of AI's impact on human life [25][27].
世纪华通谢斐:在“三大平衡”中领跑,实现游戏行业更高质量的发展
量子位· 2025-08-01 04:23
Core Viewpoint - The Chinese gaming industry, while a global leader, faces three critical contradictions that need to be balanced for higher quality development [1]. Group 1: Balancing Performance and Value - The gaming industry has regained growth momentum after adjustments, but its value is not fully recognized by society, leading to lower valuations compared to the "new consumption" sector [3]. - Bridging the value gap requires high-quality innovative products, including the release of premium titles and a proactive approach to market exploration [3]. - The industry must actively demonstrate its technological contributions and build communication bridges to convey its positive value, exemplified by the "Dragon Cup" competition initiated by Century Huatong [3]. Group 2: Balancing Emotional Value and Brand Value - The current trend favors "emotional value," with gaming serving as an excellent medium for entertainment and stress relief, but it must evolve into sustainable "brand value" with cultural depth and social recognition [4]. - Successful gaming brands in Japan, like Doraemon and Mario, symbolize cultural strength, while China still has a long way to go in establishing culturally impactful gaming brands globally [4]. - To empower emotional and brand value, the industry must protect intellectual property and integrate national values into product culture [4]. Group 3: Balancing Simple Answers and Complex Questions - The emergence of AI, such as ChatGPT, has made complex problems easier to solve, but the ability to pose high-level questions and possess scientific thinking is becoming a rare asset [5]. - Historical productivity revolutions lead to "human-machine collaboration," focusing on how humans can work with AI, with Century Huatong enhancing development efficiency through AI tools [5]. - Maintaining content originality is crucial, as AI's tendency to average content may reshape human thinking; thus, supporting incremental content creation is essential [5].
AI时代如何把想象力变成一种竞争优势?
3 6 Ke· 2025-07-31 11:57
Group 1 - The discussion focuses on how to transform human imagination into a competitive advantage in the AI era [1] - The future landscape of AI content over the next 3 to 5 years is anticipated to involve significant changes in production tools and user experiences [10][11] - AI is expected to reshape enterprise workflows and value delivery, moving from traditional tools to intelligent agents that can deliver results [12][13] Group 2 - Companies are exploring how AI can enhance creativity and storytelling, allowing individuals to express their unique experiences and narratives [17][18] - The emergence of user-generated content (UGC) is predicted, with individuals potentially creating their own intelligent IPs [6][7] - The role of AI in entertainment is seen as a catalyst for new forms of content creation and interaction, leading to a richer user experience [10][11] Group 3 - The collaboration between humans and AI is evolving, with AI taking on roles beyond mere tools, potentially acting as co-creators or project managers [20][21] - Concerns are raised about the potential dilution of human creativity and the averageization of content due to AI's influence [31][32] - The balance between AI's capabilities and human creativity is crucial, as the unique human experience remains irreplaceable [17][18] Group 4 - The future of enterprise services is expected to shift towards intelligent agents that can autonomously deliver results, enhancing efficiency and user engagement [12][13] - The integration of AI into various industries is anticipated to create new opportunities for innovation and creativity [10][11] - The potential for AI to redefine the nature of work and entertainment is a central theme, with implications for how individuals find meaning and value in their contributions [34][35]
一个“蠢问题”改写模型规则!Anthropic联创亲曝:瞄准Claude 5开发爆款应用,最强模型的价值会让人忽略成本负担
AI前线· 2025-07-30 09:09
Core Insights - The core argument presented by Jared Kaplan emphasizes the significance of Scaling Law in the development of AI models, suggesting that the majority of AI's value comes from the most powerful models, and that the current rapid evolution of AI is unbalanced, focusing more on capabilities than costs [1][6][50]. Group 1: Scaling Law and AI Development - Scaling Law is derived from fundamental questions about the importance of data size and model scale, revealing a consistent trend where increasing the scale of pre-training leads to improved model performance [10][13]. - Both pre-training and reinforcement learning phases exhibit clear Scaling Laws, indicating that as computational resources increase, model performance continues to enhance [14][17]. - The ability of AI models to handle longer tasks is increasing, with research indicating that the time span of tasks AI can autonomously complete doubles approximately every seven months [20][23]. Group 2: Future Implications and Recommendations - The future of AI may involve models capable of completing complex tasks that currently require extensive human effort, potentially revolutionizing fields like theoretical physics [25]. - Companies are encouraged to build products that are not yet fully operational, as rapid advancements in AI capabilities may soon enable these products to function effectively [29]. - Integrating AI into existing workflows and identifying new areas for large-scale application are crucial for maximizing the potential of AI technologies [30][31]. Group 3: Claude 4 and Its Enhancements - Claude 4 has improved its performance in programming tasks and has enhanced its memory capabilities, allowing it to retain information over longer interactions [34][35]. - The model's ability to understand nuanced supervision signals has been refined, making it more responsive to user instructions and improving the quality of its outputs [34][36]. Group 4: Challenges and Considerations - The current rapid advancement of AI presents challenges, as the focus on capability may overshadow the need for cost efficiency and balance in AI development [50][51]. - The potential for AI to replace human tasks raises questions about the future roles of individuals in the workforce, emphasizing the importance of understanding AI's workings and integrating it effectively into practical applications [52].
科学与健康|机“慧”共生 人形机器人在2025世界人工智能大会展现澎湃动力
Xin Hua She· 2025-07-27 10:46
Core Insights - The World Artificial Intelligence Conference in Shanghai showcased over 60 intelligent robots, highlighting advancements in both industrial and service applications [2][4] - The event emphasized the transition of humanoid robots from mere demonstrations to practical solutions in various sectors, indicating a significant shift towards commercialization [6][9] Group 1: Industry Developments - The exhibition area exceeded 70,000 square meters for the first time, featuring robots capable of diverse tasks, from combat to service [2][3] - Shanghai Electric introduced a humanoid robot designed for industrial environments, capable of autonomous identification and handling of various box sizes, enhancing warehouse efficiency [3] - The XMAN-F1 service robot by Qianlang Intelligent showcased its ability to prepare popcorn and customize drinks, indicating the potential for future service applications [4] Group 2: Technological Advancements - The progress in robot components, such as tactile grippers using innovative sensory technology, allows robots to handle fragile items with precision [5] - The collaboration between large models for task planning and small models for execution is expected to advance robotic capabilities significantly [7] Group 3: Human-Robot Interaction - The conference highlighted the importance of understanding in human-robot collaboration, suggesting that true cooperation relies on mutual comprehension of intentions [9] - Industry leaders emphasized the need for ethical frameworks and policies to ensure the safe integration of robots into society, moving beyond their role as mere tools [9][10]
三工视频 · 新360行之生成式人工智能导演丨AI不是对手,而是超级助手
Huan Qiu Wang Zi Xun· 2025-07-26 07:55
Core Insights - The rapid development of AI technology has transitioned from simple image generation to high-quality video content creation, leading to the emergence of generative AI directors in the film industry [1][3] - The film "Tracing Back" directed by Wang Xingjian and Yang Xiaolu showcases the full integration of AI in the filmmaking process, raising questions about the core value of directors when machines can generate images [1][3] Group 1: AI in Filmmaking - Generative AI is now used throughout the entire production process, from script visualization to final rendering, demonstrating the speed of technological iteration [1] - The directors emphasize that while AI enhances efficiency, emotional expression still requires human intervention, with AI improving animation production efficiency by 50% [3][5] Group 2: Human-AI Collaboration - The directors express cautious optimism regarding the potential of AI, suggesting that human emotions and feelings will guide AI's use in filmmaking [5] - A collaborative model is envisioned where AI handles bulk rendering, allowing humans to focus on emotional decision-making, potentially reshaping the film industry workflow [5] Group 3: Philosophical Reflections - The directors reflect on the essence of creativity, with Wang comparing AI's role to that of photography and painting, suggesting that directors should capture genuine emotional moments [5] - Yang views AI as an "accelerator of imagination," emphasizing its value in freeing creators from repetitive tasks to focus on more creative narrative construction [5]
弗吉尼亚大学提出Moving Out:实现物理世界人机无缝协作!
具身智能之心· 2025-07-25 07:11
Core Insights - The article emphasizes the need for a benchmark that simulates physical interactions and diverse collaboration scenarios to enhance the adaptability and generalization capabilities of intelligent agents in human-robot collaboration [3][6]. Group 1: Key Innovations - Introduction of the Moving Out benchmark, a physically-grounded human-robot collaboration environment that simulates various collaborative modes influenced by physical properties and constraints [8]. - Design of two evaluation tasks aimed at assessing the adaptability of intelligent agents to human behavioral diversity and their ability to generalize to unknown physical properties [10][11]. - Proposal of the BASS method, which enhances collaboration performance in physical environments through behavior augmentation, simulation, and action selection [13][14]. Group 2: Experimental Results - The BASS method demonstrated superior performance in both AI-AI and human-robot collaboration compared to baseline methods such as MLP, GRU, and Diffusion Policy [15][18]. - Evaluation metrics included Task Completion Rate (TCR), Normalized Final Distance (NFD), Waiting Time (WT), and Action Consistency (AC), with BASS showing significant improvements in these areas [16][17]. - User studies indicated that BASS significantly outperformed Diffusion Policy in terms of usefulness and physical understanding, reducing issues like object handover failures and delays in assistance [18]. Group 3: Related Work - Existing human-AI collaboration research has limitations, and Moving Out addresses these by providing a physically-grounded environment, diverse collaboration modes, and continuous state-action spaces [19][21]. - Previous works often focused on discrete environments with limited physical attributes or lacked independent task division, highlighting the need for more comprehensive evaluation methods that consider physical interactions [21].