（文/观察者网 张广凯 编辑/吕栋） 以魔幻交通路况著称的重庆，拥有了一家本地智能辅助驾驶供应商。 9月28日晚，千里科技在重庆举办品牌发布会，正式推出了这家智驾新军的品牌标识、英文名和未来规划。而就在当天上午，重庆市委书记袁家军主持召开 推动智能网联新能源汽车之都建设座谈会，千里科技与长安汽车、赛力斯等一起作为龙头企业发言，显示出重庆对这家企业的高度重视。 作为股东方之一的吉利董事长李书福，当晚也亲临发布会，对千里科技董事长印奇说出"这是属于你的时代"。 除了外界的期许，千里科技自身也在不断释放高调信号，此前，千里智驾董事长兼CEO王军表达了"三分天下有其一"的野心。 在此次发布的"千里计划（the Afari Plan）"中，千里科技更是提出了"AI+车+机器人"的平台级AI宏大愿景，希望未来能够全面布局家庭和工业AI场景。 但外界一直心存疑虑。在国内L2+级智驾头部效应愈发明显的格局下，由"AI 1.0"公司脱胎而来的千里科技，是否真有实力后来居上？ 其实，穿透千里科技的公关语言，我们还是能够看到一条清晰且潜力巨大的路线规划——英文品牌不是无的放矢，千里早已把客户瞄准了海外车企。 观察者网此前报道，印奇 ...

Group 1 - The article discusses the challenges faced by humanoid robots in achieving dexterity despite significant investments from venture capital firms and large tech companies [2][3][5] - Humanoid robots are designed to mimic human body structures and perform tasks in human environments, with the goal of creating versatile robots capable of handling various jobs [5][6] - Companies like Tesla and Figure are optimistic about the economic potential of humanoid robots, with predictions of generating trillions in revenue, but the timeline for achieving human-level dexterity remains uncertain [6][7] Group 2 - The history of humanoid robot development spans over six decades, with significant contributions from various researchers and institutions, including early models from Waseda University and Honda [8][9] - Despite advancements, no humanoid robot has demonstrated significant dexterity comparable to human capabilities, and existing designs have not been successfully applied in practical industrial settings [20][21] - The article emphasizes the importance of tactile feedback and dexterity in humanoid robots, arguing that current training methods relying on visual data are insufficient for achieving the desired level of skill [23][24][44] Group 3 - The article critiques the reliance on "learning from demonstration" methods, highlighting the limitations of current approaches that do not incorporate tactile or force feedback [23][24][25] - Companies like Figure and Tesla are shifting towards training humanoid robots using first-person videos of humans performing tasks, betting on the effectiveness of visual learning [26][27] - The article concludes that achieving true dexterity in humanoid robots will require a deeper understanding of tactile perception and the integration of such feedback into training methodologies [44][45]

Core Viewpoint - Despite significant investments from venture capital firms and large tech companies, humanoid robots still struggle to achieve dexterity, which is essential for performing tasks in human environments [2][3][4]. Group 1: Historical Context of Humanoid Robots - The concept of humanoid robots has been explored for over 65 years, with early developments including a computer-controlled robotic arm capable of stacking blocks in 1961 [3]. - The evolution of humanoid robots has seen contributions from various institutions, including WABOT-1 from Waseda University in the 1970s and Honda's ASIMO in 2000 [11][12]. Group 2: Current State and Future Predictions - Humanoid robots are currently in the early stages of development, with Gartner indicating they have not yet reached their peak hype [4]. - Companies like Tesla and Figure are optimistic about the economic potential of humanoid robots, with predictions of creating trillions in revenue [9][10]. Group 3: Challenges in Dexterity - Achieving human-level dexterity in humanoid robots remains a significant challenge, as current robotic hands lack the necessary finesse and adaptability for a wide range of tasks [23][24]. - Existing methods for training robots often rely on visual demonstrations, which do not adequately capture the tactile feedback necessary for dexterous manipulation [27][28]. Group 4: Learning Approaches - The industry has seen a shift towards end-to-end learning methods, where robots learn from observing human actions, but this approach has limitations due to the lack of tactile feedback and precision [30][31]. - Successful applications of end-to-end learning in other fields, such as speech recognition and image labeling, highlight the importance of pre-processing and human-like structures in achieving effective learning outcomes [49][50]. Group 5: Importance of Tactile Feedback - Human dexterity is heavily reliant on rich tactile feedback, which current humanoid robots do not possess, leading to challenges in replicating human-like manipulation [51][52]. - The complexity of human touch perception and the integration of multiple body parts in dexterous tasks further complicate the development of humanoid robots capable of similar actions [52].

资料图。本文来源：澎湃新闻 记者 范佳来 在新模型的研究过程中，需要设计和实现很多新的GPU算子，使用高级语言TileLang进行快速原型开发，以支持更深入的探索。在最后阶 段，以TileLang作为精度基线，逐步使用底层语言实现更高效的版本。此次开源的主要算子包含TileLang与 CUDA两种版本。 DeepSeek表示，得益于新模型服务成本的大幅降低，官方API价格也相应下调，开发者调用DeepSeek API的成本将降低50%以上。从价格 来看，输入缓存命中从0.5元降至0.2元/百万tokens，缓存未命中从4元降至2元/百万tokens，输出由12元降至3元/百万tokens。 DeepSeek新模型降价：优化推理效率，API价格降超50% DeepSeek发布新版本模型，调用API成本降低超50%。 9月29日，DeepSeek正式发布DeepSeek-V3.2-Exp模型，这是一个实验性（Experimental）的版本。 值得注意的是，此前有不少科技博主发现，DeepSeek-V3.2新模型已上传至其HuggingFace官方页面，随后被删除，此后DeepSeek正式公告 新版本的推出。 据 ...

2025年9月30日，有人给TOP2说i6可能发战报，这个属于消息层面的事。 目前结合消息面与推理面，TOP2仍然倾向理想可能发i6战报，可能不发，可能发的概率大一些。 TOP2总体倾向吸引认可TOP2推理层面价值的读者，不希望吸引希望获得各种非公开信息的读者。 分析判断理想实际经营动作的锚点之一是，只要李想还是内核精神上的实际控制人（这点与法律或规章层面的实际控制人有所区别），那么理想唯一不 变的只有以挑战成长的极限作为最终驱动力，其他都可能变。TOP2挺早就在说，更长时间周期，理想未必会坚持移动的家，幸福的家，或者家用车理 念。这次i6整体宣发上，就没有小孩带娃元素。 其实家用车这个定义非常宽泛，所有非主要商用的，其实都可以归为家用范畴，此前理想对家用定义比较局限于要带12岁以下的小孩。 理想在做的是，以挑战成长的极限为奖励函数的强化学习。具体的经营策略就是会依据环境的反馈变来变去的。 理想的确是倾向不发锁单/大定战报的，原因之一是理想一方面全直营，方面整体诚实度高，导致理想如果要发，只能发真数据，另一方面理想的大定销 量曲线上，整体属于早期大定数据看起来很不亮眼，这导致发了也起不到什么好效果。 2025 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Dapeng Zhang等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 | | | 1. 介绍 机器人学长期以来一直是科学研究中的重要领域。早期的机器人主要依赖预编程的指令和人工设计的控制策略来完成任务分解与执行。这类方法通常应用于简 单、重复性的任务，例如工厂流水线和物流分拣。近年来，人工智能的快速发展使研究者能够在图像、文本和点云等多模态数据中，利用深度学习的特征提取与 轨迹预测能力。通过结合感知、检测、跟踪和定位等技术，研究者将机器人任务分解为多个阶段，以满足执行需求，从而推动了具身智能与自动驾驶的发展。然 而，大多数机器人仍然作为孤立的智能体存在，它们通常为特定任务而设计，缺乏与人类和外部环境的有效交互。 为克服这些局限性，研究者开始探索将大语言模型（LLMs）与视觉语言模型（VLMs）引入机器人操作中，以实现更精准和灵活的控制。现代的机器人操作方法 通常依赖视觉-语言生成范式（如自回归模型 或扩散模型），并结合大规模数据集 以及先进的微调策略。我们将这些方法称为 VLA基础模型，它们 ...

Core Insights - Reinforcement Learning (RL) is transitioning from a niche area to a critical component in advancing reasoning, decision-making, and complex scene interactions, especially as developments in Large Language Models (LLMs) reach a bottleneck [3] - The current moment is pivotal for the cross-disciplinary integration of RL, with academia, industry, and startups collaborating to move RL from research to practical applications [3] Event Details - An event is scheduled for October 8th at 6:30 PM in San Francisco, featuring top-tier guests from academia, industry, and entrepreneurship to discuss the future of RL [4] - Notable speakers include Zeng Dong from UCSB, Qifei Wang from DeepMind, Bill Zhu from Pokee AI, and others who are shaping the next generation of RL [6][7] Organizers and Community - The event is presented by Z Potentials in collaboration with HatTrick Capital and Future Builderz, focusing on supporting early-stage technology entrepreneurs and bridging the gap between research and industry [8][9] - HatTrick Capital is a Silicon Valley fund dedicated to backing new generation technology entrepreneurs, particularly in the AI sector [9] Networking Opportunities - The event will provide a relaxed networking atmosphere, allowing attendees from leading labs like OpenAI, Anthropic, DeepMind, and Meta to engage in deep discussions [10]

9月28日，上汽通用别克品牌至境L7正式上市，共推出5款车型，限时权益价16.99万元~21.59万元。作为别克高端新能源子品牌"至境"的首款旗舰轿 车，至境L7诞生于别克"逍遥"超级融合架构，搭载"真龙"增程系统、"逍遥智行"辅助驾驶系统，全球首发基于"强化学习"的Momenta R6飞轮大模型，以及高 通最新一代SA8775P芯片赋能的豪华智能座舱。 即日起，用户可通过别克官网、iBuick App、Buick小程序、别克抖音直播间，或别克全国授权经销商门店下定。在10月31日前完成线上下定并支付定 金，且在12月31日完成车辆交付和开票的用户，即可享受限时权益价及丰厚限时上市权益。 | 刷克至境 L7 | 官方指导价(元) | 限时权益价(元) | | --- | --- | --- | | 尊享版 | 173, 900 | 169, 900 | | 奢享版 | 0000"881 | 182, 900 | | 尊享逍遥智行版 | 000 366 | 189, 900 | | 磨享逍遥智行版 | 206 000 | 202, 900 | | 艾维亚 | 200 000 | 215, 900 | | 车身颜 ...

9月28日，上汽通用别克品牌至境L7正式上市，共推出5款车型，限时权益价16.99万元~21.59万元。 至境L7还提供全类型车位泊车辅助，支持包括标准车位、极窄车位、机械车位、断头路垂直/水平泊入 在内的多种复杂场景，打破"停车焦虑"。至境L7全球首发搭载高通最新一代SA8775P芯片，以高达72 TOPS的AI算力专为智舱服务，配合行业顶级软硬件系统，不仅提供适配不同智慧出行场景的沉浸、自 然交互体验，还可通过持续学习和快速迭代，实现车机体验的"智能成长"。 拥有5032mmx1952mmx1500mm的阔绰车身尺寸、溜背式动感设计，至境L7呈现C级中大型豪华轿车的 磅礴气势。超静谧NVH全车无框车门、百万级豪车同款银河星空展翼大灯、星轨浮光展翼尾灯，加上 车顶激光雷达，以及标志"逍遥智行"的小蓝灯，将科技融入优雅。 至境L7搭载越级豪华底盘，采用前双叉臂、后五连杆悬架结构，后悬采用6球头设计。RTD连续阻尼可 变悬架可通过毫秒级阻尼调节，主动控制车身姿态，显著提升乘坐舒适性、平顺性和操纵稳定性。前悬 架下控制臂和后副车架上布置了仅少数豪华车型才会配备的液压衬套，直径达90mm。 至境L7搭载"真龙"增程 ...

Core Insights - The article highlights three main advantages for China's development in intelligent driving: scenario advantage, ecosystem advantage, and policy advantage [1] - The integration of scenario advantages with advanced intelligent driving platforms marks the transition to the 2.0 stage of automotive intelligence [1] Group 1: Intelligent Driving Technology - Horizon's HSD (Horizon SuperDrive) solution has significantly improved urban driving capabilities, providing a smoother, more human-like, and reliable experience [4] - The "end-to-end + reinforcement learning" architecture is a key highlight of the HSD upgrade, enabling low latency and enhanced safety and efficiency [4][5] - The system's ability to process complex scenarios without modular segmentation allows for a more fluid driving experience, akin to human control [5] Group 2: System Performance - HSD demonstrates ultra-low latency and strong defensive driving capabilities, with rapid responses to unexpected situations such as construction zones and sudden obstacles [6][7] - The system's performance includes smooth control in various driving conditions, maintaining stability and fluidity even in complex traffic scenarios [7] Group 3: Safety and Certification - Horizon has established the largest active safety testing scenario database in the industry, covering over 30,000 scenarios and achieving over 10 million kilometers of testing [10] - The company has received the world's first and only ISO 8800 AI functional safety certification, enhancing its credibility in the global market [10]