通用机器人

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中美AI机器人竞争激烈,日本欲卷土重来
日经中文网· 2025-10-12 00:34
宇树科技的展台上进行拳击比赛的人形机器人(8月,北京市) 截至2025年5月,人形机器人企业32%在美国,27%在中国,日本没有进入前5名。美国特斯拉和英伟达 加紧研发,中国的初创企业猛追。日本占工业机器人生产份额的逾3成,想借助这一优势在物理AI领域 卷土重来…… 人工智能(AI)机器人的开发竞争日趋激烈。10月8日,软银集团(SBG)宣布收购瑞士工业 巨头ABB的机器人业务。在美国,纯电动汽车(EV)巨头特斯拉和半导体巨头英伟达加紧研 发,中国的初创企业也在猛追。作为AI普及期有潜力的市场,机器人产业的主导权争夺将日趋 激烈。 特斯拉的人形机器人"擎天柱(Optimus)" (Reuters) "擎天柱(Optimus)将占据特斯拉80%的价值",特斯拉的首席执行官(CEO)马斯克9月 在X上发表了这样的贴文。擎天柱是特斯拉积极发展的人形机器人。 马斯克描绘的未来是,到2040年将有100亿台价值2万~2.5万美元的人形机器人在运行。 以往的机器人无法彻底处理通过摄像头和传感器获取的周边信息,只能依靠人工操作。随着 搭载尖端AI,将诞生无需人类操作就能实时学习和行动的通用机器人。发挥大脑作用的AI和作 为 ...
刚刚,Figure 03惊天登场,四年狂造10万台,人类保姆集体失业
3 6 Ke· 2025-10-10 10:50
通用机器人曙光来临!今天,Figure 03正式亮相,专为Helix「大脑」量身打造,冰冷机身有了织物外覆。更值得一得的是,03手掌心配备一 颗摄像头,指尖即可感知3克的力。 Figure 03出世了! 时隔一年,Figure终于带来了下一代人形机器人Figure 03,正式开启通用机器人规模化时代! 这款人形机器人,专为Helix、家庭使用,以及全球规模化应用而设计。 从外观设计上看,Figure 03做了大幅升级,尤其是全机采用了「柔性织物外层」,取代机械外壳。 就连每只手掌心,都集成了一个广角摄像头。 它不仅能执行类人任务,还能通过与人类的互动直接学习,展现出前所未有的智能与适应性。 从浇花、端茶倒水,到收拾家务、陪孩子玩各种琐碎的事情,它都能胜任。 更令人惊叹的是,Figure 03每个指尖可以感知「3克的力」。 甚至,就连放在指尖的一枚回形针的重量,都可以察觉到。 今天,Figure 03还登上了TIME封面。 CEO Brett Adcock表示,「未来,每个家庭都将拥有一个人形机器人」。 这一次,Figure 03进化主要有四大亮点: Helix:配备全新设计的传感套件和手部系统,专为激活Hel ...
灵猴机器人完成超亿元A轮融资,TCL创投等共同领投
Xin Lang Cai Jing· 2025-09-29 05:22
Group 1 - Suzhou Linghou Robot Co., Ltd. has completed over 100 million yuan in Series A financing [1] - The financing round was led by Jinding Capital, Boyuan Capital, and TCL Venture Capital, with participation from multiple investment institutions including Suzhou Venture Capital, Dongyun Venture Capital, Caitong Capital, and Yinxinggu Capital [1] - The funds raised will primarily be used for the research and development of core components in industrial automation and general robotics, laboratory construction, and capacity expansion [1]
腾讯研究院AI速递 20250925
腾讯研究院· 2025-09-24 16:01
Group 1: AI Tools and Applications - Google has launched the Mixboard, an AI drawing tool supported by Nano Banana, allowing users to visualize ideas instantly using natural language [1] - Alibaba introduced the Wan2.5 Preview model, which can generate synchronized audio-visual videos, supporting 1080P HD video at 24 frames per second [2] - Kuaishou's Keling 2.5 Turbo model has significantly reduced costs by nearly 30% while improving the quality of generated sports action videos [3] - Mita AI has unveiled the "Agentic Search" mode, enabling users to perform multiple tasks simultaneously through a new search paradigm [4] - Suno has released its V5 model, claiming to be the most powerful music generation model to date, offering studio-quality sound [5][6] Group 2: Robotics and AI Development - Wang Xingxing from Yushu Technology highlighted the challenges in general robotics, including cable issues and AI chip power limitations [8] - The Google Cloud AI entrepreneur report emphasizes the importance of speed and innovation as core competitive advantages in the AI era [9] Group 3: AI Chip Market Dynamics - NVIDIA's investment of $5 billion in Intel is expected to reshape the PC and data center markets, posing a significant threat to AMD and ARM [10] - Huawei is emerging as a strong competitor in the AI chip sector despite facing U.S. sanctions, making progress in 7nm chips and custom HBM [10] - AI computing expenditure is projected to rise from $360 billion to approximately $500 billion, with Oracle capitalizing on major clients like OpenAI [10] Group 4: Future of AI Infrastructure - Sam Altman envisions a future where AI becomes a fundamental economic driver and a basic human right, proposing the establishment of factories to produce AI infrastructure [12] - He emphasizes that increasing computing power is key to generating revenue and plans to build substantial AI infrastructure in the U.S. [12]
大模型之后看机器人?Sergey Levine谈通用机器人规模化落地的真实瓶颈与破局方案
锦秋集· 2025-09-15 12:37
Core Insights - The core prediction is that by 2030, robots capable of autonomously managing entire households will emerge, driven by the "robot data flywheel" effect [1][11]. Group 1: Robot Development and Implementation - Robots are expected to be deployed faster than autonomous driving and large language models due to their ability to quickly obtain clear feedback from the physical world [2]. - The clear technological path involves an integrated model of "vision-language-action," allowing robots to understand tasks and plan actions autonomously [3]. - Real-world applications in small-scale settings are prioritized over large-scale simulations to leverage precise data feedback [4]. Group 2: Emerging Capabilities and Challenges - "Combination generalization" and "emergent abilities" will lead to significant advancements in robot technology, enabling robots to transition from specific tasks to general household capabilities [5]. - Current challenges in robot development include response speed, context memory length, and model scale, but these can be addressed by combining existing technologies [6]. - The rapid decrease in hardware costs has lowered the entry barrier for AI entrepreneurs, allowing small teams to quickly iterate and validate market needs [7]. Group 3: Future Vision and Timeline - The ultimate goal for robots is to execute long-term, high-level tasks autonomously, requiring advanced capabilities such as continuous learning and problem-solving [10]. - The "flywheel effect" will accelerate robot capabilities as they perform useful tasks and gather experience data [11]. - Predictions suggest that within one to two years, robots will start providing valuable services, with fully autonomous household management achievable in about five years [11]. Group 4: Comparison with Other Technologies - The development of robots may progress faster than large language models and autonomous driving due to the unique nature of their interaction with the physical world [12][13]. - Robots can learn from clear, direct human feedback in physical tasks, contrasting with the challenges faced by language models in extracting effective supervisory signals [12]. Group 5: Learning and Data Utilization - Robots benefit from embodied intelligence, allowing them to focus on relevant information while learning from vast amounts of video data [20][21]. - The ability to generalize and combine learned skills will be crucial for achieving general intelligence in robots [23][25]. Group 6: Systemic Challenges and Solutions - The "Moravec's Paradox" highlights the difficulty of replicating simple human tasks in robots, emphasizing the need for physical skill development over memory expansion [26][27]. - Future advancements will require addressing the trade-offs between reasoning speed, context length, and model scale [28][29]. Group 7: Hardware and Economic Factors - The cost of robotic hardware has significantly decreased, enabling broader deployment and data collection for machine learning [33]. - The economic impact of automation will enhance productivity across various sectors, necessitating careful planning for societal transitions [34]. - Geopolitical factors and supply chain dynamics will play a critical role in the advancement of robotics, emphasizing the need for a balanced ecosystem [35].
英伟达推出的“大脑”, 能让机器人变聪明吗?
第一财经· 2025-08-26 03:25
Core Viewpoint - Nvidia has launched the Jetson Thor platform, significantly enhancing the computational power for robotics, which is essential for running advanced AI models and improving robot efficiency [2][3][4]. Group 1: Product Launch and Specifications - The Jetson Thor platform offers a computational power of 2070 TFLOPS at FP4 precision, a substantial increase compared to previous models like Jetson TK1 and Jetson Orin [2]. - Jetson Thor's AI performance is 7.5 times greater than that of Jetson Orin, with energy efficiency improved by 3.5 times [3]. - The platform is built on the Blackwell architecture, aligning with Nvidia's latest GPU architecture used in data centers [2]. Group 2: Market Demand and Applications - There is a growing demand for higher computational power in robotics, as many developers are currently using multiple Orin chips to meet their needs [4]. - The Jetson platform has around 2.2 million developers and over 7,000 companies utilizing Orin, indicating a strong market presence [5]. - Companies in China, such as Zhiyuan Robotics and Youbix, are already preparing to adopt the Thor platform [5]. Group 3: Industry Trends and Future Outlook - The global humanoid robot market is projected to reach 2.562 billion yuan in 2024, with significant growth expected by 2031 [6]. - Nvidia is focusing on three areas in robotics: humanoid robots, autonomous vehicles, and robotic applications in large spaces like factories and cities [5]. - The competition in the robotics "brain" sector is intensifying, with companies like Tesla also developing their own computing solutions for humanoid robots [6].
助力机器人应用设计!英伟达(NVDA.US)推出新计算平台Jetson Thor
Zhi Tong Cai Jing· 2025-08-26 02:24
Core Insights - Nvidia officially launched its Jetson Thor computing platform designed for robotics applications, along with the Jetson AGX Thor developer kit and the Jetson T5000 production module [1][2] - The new Jetson AGX Thor developer kit starts at $3,499, and it is built on Nvidia's Blackwell architecture, offering significant performance improvements over the previous Jetson Orin products [1] - AI computing performance has increased by 7.5 times compared to the previous generation, with CPU performance up by 3.1 times and memory capacity doubled to 128GB [1] Performance Enhancements - The Jetson Thor platform allows developers to process high-speed sensor data and perform visual inference in real-time within dynamic environments, addressing previous speed limitations [1] - It is specifically designed for generative inference models, supporting next-generation physical AI agents powered by large transformer and visual language models to operate in real-time at the edge [1] Market Impact and Adoption - Nvidia's CEO highlighted that Jetson Thor is aimed at millions of developers to help build robotic systems that can interact with and even change the physical world [2] - The platform addresses significant challenges in the robotics field, enabling real-time operation of multiple AI workflows for intelligent interaction with humans and the physical environment [2] - Over 2 million developers are currently using Nvidia's Jetson platform and robotics technology stack across various industries, including manufacturing, logistics, healthcare, and agriculture [2] - Jetson Thor has already been adopted by leading companies in the industry, such as Agility Robotics, Amazon Robotics, Boston Dynamics, and Figure [2] - Nvidia is set to announce its earnings report on Wednesday after the market closes, with expectations that its performance and outlook will significantly influence market direction [2]
售价2万5!英伟达推出机器人“最强大脑”:AI算力飙升750%配128GB大内存,宇树已经用上了
量子位· 2025-08-25 23:05
Core Insights - NVIDIA has launched the Jetson Thor, a new robotic computing platform that integrates server-level computing power into robots, achieving an AI performance of 2070 TFLOPS, which is 7.5 times higher than the previous generation Jetson Orin, with a 3.5 times improvement in energy efficiency [1][3][4]. Performance and Specifications - Jetson Thor features a massive 128GB memory configuration, unprecedented in edge computing devices [2]. - The platform is built on the Blackwell GPU architecture, supporting multiple AI models simultaneously on edge devices [6]. - The Jetson AGX Thor developer kit is priced at $3499 in the U.S. (approximately 25,000 RMB), while the T5000 module is available for $2999 for bulk purchases [8][9]. Technical Features - The Jetson Thor includes advanced specifications such as a GPU with 2560 CUDA cores and 96 fifth-generation Tensor Cores, and a CPU with 14 Arm Neoverse V3AE cores, significantly enhancing real-time control and task management capabilities [11][13]. - It supports high bandwidth with 128GB LPDDR5X memory and 273GB/s memory bandwidth, crucial for large Transformer inference and high-concurrency video encoding [13]. - The platform can achieve a response time of 200 milliseconds for the first token and generate over 25 tokens per second, enabling real-time human-robot interaction [16]. Industry Adoption - Several Chinese companies, including Unisound Medical and Youbik, are integrating Jetson Thor into their systems, highlighting its impact on robot agility, decision-making speed, and autonomy [19]. - Boston Dynamics is incorporating Jetson Thor into its Atlas humanoid robot, allowing it to utilize computing power previously only available in servers [20]. - Agility Robotics plans to use Jetson Thor as the core computing unit for its sixth-generation Digit robot, enhancing its logistics capabilities [21]. Software and Development - Jetson Thor is optimized for various AI frameworks and models, supporting NVIDIA's Isaac for simulation and development, and Holoscan for sensor workflows [14]. - The platform facilitates a continuous training-simulation-deployment cycle, ensuring ongoing upgrades to robotic capabilities even after deployment [25]. Future Outlook - NVIDIA emphasizes the need for a triad of computing systems for effective physical AI and robotics: a DGX system for training, an Omniverse platform for simulation, and the Jetson Thor as the robot's brain [23].
英伟达宣布Jetson Thor已发售,宇树科技、银河通用已接入
Xin Lang Ke Ji· 2025-08-25 15:39
Core Insights - NVIDIA has launched the Jetson AGX Thor developer kit and production-grade module, designed to provide computational power for millions of robots across various industries including manufacturing, logistics, transportation, healthcare, agriculture, and retail [2] - The Jetson Thor is already being utilized by several industry leaders such as United Imaging Healthcare, Wanji Technology, UBTECH, Galaxy General, Yushu Technology, Zhongqing Robotics, and Zhiyuan Robotics [2] - NVIDIA's CEO Jensen Huang emphasized that Jetson Thor is built for millions of developers globally, enabling them to create robotic systems that can interact with and even change the physical world [2] Performance and Specifications - Jetson Thor is based on NVIDIA's Blackwell GPU and features 128GB of memory, offering up to 2,070 FP4 TFLOPS of AI computing power while operating within a power range of 130 watts [2] - Compared to its predecessor, the Jetson AGX Orin, the AI computing performance of Jetson Thor has increased by 7.5 times, and its energy efficiency has improved by 3.5 times [3] - Jetson Thor can run various generative AI models, including NVIDIA Isaac GR00T N1.5 and mainstream large language and vision-language models [3]
英伟达推出的“大脑”, 能让机器人变聪明吗?
Di Yi Cai Jing· 2025-08-25 15:36
Core Insights - The era of general-purpose robots is approaching, with NVIDIA launching the Jetson Thor computing platform, enhancing the computational power available for robotics [1][6]. Group 1: Product Launch and Specifications - NVIDIA's Jetson Thor platform offers a computational power of 2070 TFLOPS at FP4 precision, significantly higher than previous models like Jetson TK1 and Jetson Orin, which had 0.3 TFLOPS and 275 TOPS respectively [3]. - The AI performance of Jetson has improved by 7000 times over the past decade, with Jetson Thor being 7.5 times more powerful and 3.5 times more energy-efficient than Jetson Orin [3][4]. Group 2: Market Demand and Applications - There is a strong demand for higher computational power in robotics, as many humanoid robot developers require chips with greater capabilities than Orin to run large parameter models effectively [4][5]. - The Jetson platform has 2.2 million developers and over 7,000 companies using Orin, indicating a robust ecosystem for NVIDIA's robotics solutions [6]. Group 3: Industry Trends and Future Outlook - The humanoid robot market is projected to reach 2.562 billion yuan in 2024, with significant growth expected by 2031 as the industry enters a rapid growth phase [7]. - NVIDIA is focusing on three areas in robotics: humanoid robots, autonomous vehicles, and large-scale robotic applications in factories and cities, highlighting the broad potential for robotics technology [6].