Core Insights - The exploration towards Artificial General Intelligence (AGI) highlights embodied intelligence as a key direction, focusing on the interaction and adaptation of intelligent agents within physical environments [1][3] - The development of embodied intelligence is marked by the evolution of its core components, the brain and cerebellum, which are crucial for perception, task understanding, and action execution [1] Industry Analysis - In the past two years, numerous star teams in the field of embodied intelligence have emerged, establishing valuable companies such as Xinghaitu, Galaxy General, and Zhujidongli, driving advancements in embodied intelligence technologies [3] - Major domestic companies like Huawei, JD, Tencent, and Ant Group are actively investing and collaborating to build a robust ecosystem for embodied intelligence, while international players like Tesla and Wayve are focusing on industrial applications and autonomous driving [5] Technological Evolution - The evolution of embodied intelligence technology has progressed through several stages, from low-level perception to high-level task understanding and generalization [6] - The first stage focused on grasp pose detection, while the second stage introduced behavior cloning, allowing robots to learn from expert demonstrations [6][7] - The introduction of Diffusion Policy methods in 2023 marked a significant advancement, enhancing stability and generalization in task execution [6][9] - The current phase emphasizes the integration of Vision-Language-Action (VLA) models, enabling robots to understand human instructions and perform complex tasks [7][9] Future Directions - The industry is exploring the fusion of VLA models with reinforcement learning, world models, and tactile sensing to overcome existing limitations [9][11] - This integration aims to enhance robots' capabilities in long-term tasks, environmental prediction, and multi-modal perception, expanding their operational boundaries [11][12] Educational Initiatives - There is a growing demand for engineering and system capabilities in the field of embodied intelligence, prompting the development of comprehensive educational programs [19] - These programs aim to equip participants with practical skills in strategy training, simulation testing, and the deployment of advanced models [19][20]

Core Viewpoint - The integration of visual and tactile perception in robotics, termed VTLA, is seen as the next evolution in the field of embodied intelligence, addressing the limitations of current models that lack tactile capabilities essential for physical interactions [1][14]. Product Overview - The new product line from Daimeng Robotics, starting at a price of 1299 yuan, represents a comprehensive evolution across four core dimensions, setting a new industry benchmark in technology, product, and pricing [2]. - The product positioning of "beyond touch" indicates its sensory capabilities surpassing human touch, functioning not only as a tactile sensor but also as an edge AI platform aimed at enhancing user experience and advancing the global embodied intelligence industry [2]. Company Background - Daimeng Robotics, incubated at the Hong Kong University of Science and Technology, was co-founded by renowned robotics experts and has raised several hundred million yuan in multiple funding rounds since its official operation in 2023, achieving a new high in the global tactile perception field [4]. - The company has established a solid technological moat through its original monochromatic light tactile sensing technology, which addresses industry pain points such as high computational requirements and poor durability associated with traditional three-color light solutions [4]. Product Features - The DM-Tac W2 tactile sensor offers two sizes with a 55% increase in sensing area for the larger model and a 20% reduction in thickness for the smaller model, catering to diverse operational needs [6]. - The "Blade" tactile sensor features a 28° pointed design, allowing easy access to narrow or complex spaces, enhancing versatility in various scenarios [6]. - The new generation of products boasts improved electromagnetic interference resistance and is the world's first dustproof and waterproof tactile sensor, exceeding the IP65 international standard [8]. - The sensors provide three-dimensional ultra-high resolution and precise six-dimensional force information, with a new micron-level texture recognition capability for applications in precision device defect detection and material classification [10]. AI Integration - The edge AI computing platform DM-Flux can connect to five tactile sensors simultaneously, enabling direct control of grippers and dexterous hands, thus meeting complex multimodal tactile data processing needs [12]. Industry Vision - Daimeng Robotics aims to "break through the limits of physical world perception and build a harmonious world of human-machine coexistence," focusing on disruptive innovations in tactile perception and dexterous manipulation [14]. - The company is also advancing in the field of wearable remote operation devices, with plans to release a new generation of "human-centered" wearable remote operation data collection devices within the year [14].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Physical Intelligence团队 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 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 11月17号！Physical Intelligence团队正式发布 ，从经验中学习的VLA。 项目链接：https://www.pi.website/blog/pistar06 论文链接：https://www.pi.website/download/pistar06.pdf VLA模型如何通过强化学习在现实部署中实现自我改进？ 提出了一种通用方法RECAP：基于经验与校正的优势条件策略强化学习，该方法通过优势条件机制 实现VLA模型的强化学习训练。 该方法将异构数据整合到自我改进过程中，包括演示数据、在线收集数据以及在自主执行期间专家远程干预数据。RECAP方法首先通过离线强化学习预训练通用型 VLA模型（记为 ），该模型随后可通过机器人现场数据收集实现下游任务的专业化性能提升。 实验表明 ...

贝佐斯亲身下场物理AI了，亲自担任CEO的那种。 纽约时报消息，这名前世界首富创立了一家新公司并亲自担任联席CEO。 而且资金实力雄厚，包括贝佐斯本人出资在内，该公司已获得62亿美元资金。 编辑丨 量子位 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区： 具身智能之心知识星球（戳我） ，这里包含所有你想要的! 这也是贝佐斯卸任亚马逊CEO之后，首次担任正式的运营职务。 贝佐斯入局物理AI 去年，贝佐斯投资了具身大牛 Sergey Levine 创立的顶尖AI机器人公司Physical Intelligence，现在他又亲自下场创立了 Project Prometheus ，进军物理AI。 有贝佐斯的下场，这个公司一创立就资金雄厚，获得了62亿美元，约合人民币440亿。 员工规模也达到了上百人，其中包括从OpenAI、DeepMind等顶级人工智能公司挖来的研究人员。 Project Prometheus的研究项目包括将人工智能应用于机器人、药物设计和科学发现等物理任务，明确将重点放在计算机、汽车、航空航天 等高科技 ...

Core Insights - The article discusses the increasing number of submissions to various conferences and the concerns of researchers regarding the suitability of different conferences and the preferences of reviewers [1] - It highlights the active research directions in embodied intelligence, including VLN, VLA, reinforcement learning, and real2sim2real, and provides guidance for newcomers on how to choose their research focus [1][3] - The article promotes a customized paper mentoring service aimed at helping researchers navigate the complexities of paper writing and submission [3][4][5] Group 1 - The article notes that many researchers are anxious about selecting the right conference and understanding which research directions are favored by reviewers [1] - It emphasizes that humanoid robots are particularly active in reinforcement learning and sim2real/real2sim2real research, suggesting that labs with relevant embodiments should explore these areas [1] - It mentions that mechanical arm embodiments are suitable for VLA, VLA+RL, and diffusion policy research, with a high computational power requirement for VLA [1] Group 2 - The article states that quadrupedal robots are also suitable for reinforcement learning research, although there may be fewer innovative points due to prior extensive work in this area [2] - It suggests that combining VLN and VLA with mobile manipulation could be a promising research direction [3] - The article introduces a paper mentoring service that offers one-on-one customized guidance across various top-tier conference topics, emphasizing the importance of having a good idea and navigating potential pitfalls for new researchers [3][4] Group 3 - The mentoring service covers a full process from topic innovation to experimental design, code debugging, paper writing, and submission strategy, aimed at producing high-quality results quickly [4] - It highlights the dual perspective of both industrial and academic value, focusing not only on publishing papers but also on practical applications [5] - The article offers a free matching service for the first ten inquiries, allowing researchers to have in-depth meetings with mentors based on their research direction and academic background [6]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Physical Intelligence团队 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 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 11月17号！Physical Intelligence团队正式发布 ，从经验中学习的VLA。 项目链接：https://www.pi.website/blog/pistar06 论文链接：https://www.pi.website/download/pistar06.pdf VLA模型如何通过强化学习在现实部署中实现自我改进？ 提出了一种通用方法RECAP：基于经验与校正的优势条件策略强化学习，该方法通过优势条件机制 实现VLA模型的强化学习训练。 该方法将异构数据整合到自我改进过程中，包括演示数据、在线收集数据以及在自主执行期间专家远程干预数据。RECAP方法首先通过离线强化学习预训练通用型 VLA模型（记为 ），该模型随后可通过机器人现场数据收集实现下游任务的专业化性能提升。 实验表明 ...

Core Viewpoint - The article introduces the Imeta-Y1, a lightweight and cost-effective robotic arm designed for beginners and researchers in the field of embodied intelligence, emphasizing its accessibility and ease of use for algorithm validation and project development [3][4][6]. Product Features - The Imeta-Y1 robotic arm is designed with a compact structure and modular interfaces, making it suitable for embedded AI and robotics learning platforms [7]. - It offers a full-process open-source toolchain and code examples, supporting data collection, model training, and deployment [18][30]. - The arm supports dual-language interfaces (Python and C++) and is compatible with ROS1 and ROS2, facilitating quick onboarding for users [4][19][20]. Technical Specifications - The robotic arm has a weight of 4.2 kg, a rated load of 3 kg, and 6 degrees of freedom, with a working radius of 612.5 mm and a repeat positioning accuracy of ±0.1 mm [9][20]. - It operates at a supply voltage of 24V and communicates via CAN, with a control method that includes trajectory tracking and teaching [9][20]. Development and Support - The company provides a comprehensive open-source SDK, including drivers, API interfaces, sample code, and documentation, supporting rapid application development [27]. - The product includes a 24-hour quick response for after-sales support, ensuring users receive timely assistance [20][45]. Testing and Reliability - The robotic arm undergoes rigorous hardware testing processes, including precision calibration, durability, load performance, and stability verification, to ensure reliability and safety in various application scenarios [36][40][41].

但还有相当多的同学卡住了，比如算力的问题，数据采集的问题，还有模型优化、项目实战的问题等。关于算 力，前面分享过很多轻量化的方法，也能做出不错的性能，甚至SOTA，这能够适配一些算力不足的同学。 近期开了一个小范围的线上会，和大家唠了一会儿近期的状态。一些同学能抓到关键的部分，跟着社区里面的 路线进步较快。即使用低成本的硬件方案，也能做出不错的效果，有的同学甚至已经把act和pi0部署上去了。 数据采集部分，建议大家先从基础的遥操作尝试，重点关注数据的质量，噪声数据，可能导致模型训不出效 果，特别是大多数数据都是噪声数据。数据量不够，可以尝试real2sim2real系列方法。 模型优化部分，对一些使用机械臂的同学，可以尝试RL+VLA方案，但人形和自由度多的本体，建议不要轻易 入坑，效果难做出。关于一些好的开源项目，已经汇总到社区内部，大家可以照着教程复现。 以上为我们的具身社区： 具身智能之心知识星球 的分享，也欢迎更多需要入门进阶的同学加入我们的社区。 近一年的搭建，社区内已经完成了技术路线分享、直播、问答、求职、赛事等多个版块的分享。实现了产业、 学术、求职、问答交流等多个领域的闭环。社区致力于为行业 ...

Core Viewpoint - The automotive industry is increasingly entering the humanoid robot sector, driven by the need for industrial upgrades and new valuation anchors following the decline of the new energy capital narrative. The competition will hinge on cross-system integration capabilities and capital endurance rather than breakthroughs in individual technologies [2][15][16]. Group 1: Industry Participation - A significant number of automotive companies, including GAC, SAIC, BYD, Changan, and Chery, have entered the humanoid robot race, with Tesla and BMW also proposing their own humanoid robot concepts [2][4]. - Since 2025, nearly 30 automotive parts companies in A-shares have established robot subsidiaries, focusing on key components such as dexterous hands and sensors [10][9]. - The automotive sector's strategic commitment to humanoid robots aims to enhance production efficiency and reduce costs, while also allowing for flexible capacity adjustments in response to market fluctuations [6][15]. Group 2: Company Strategies - Automotive companies can be categorized into four groups based on their approach to the robot sector: self-research, investment and acquisition, scenario-driven, and a combination of investment and self-research [7]. - Companies like Tesla, Xpeng, and GAC are leading the self-research approach, treating robots as a core strategic focus alongside smart electric vehicles [7]. - The investment and acquisition group, represented by Hyundai, BMW, and Mercedes-Benz, seeks to quickly fill technological gaps through investments or acquisitions [7]. - Scenario-driven companies, such as BAIC and Chery, focus on developing customized humanoid robots in collaboration with robot firms to accelerate commercial application [8]. Group 3: Component Development - Major automotive parts manufacturers are also investing in the humanoid robot sector, with companies like Top Group investing 5 billion yuan to establish a production line for robot electric drive systems, aiming for an annual capacity of 300,000 sets [13]. - The integration of advanced technologies such as AI and big data into the robot industry is expected to create disruptive products, with significant development potential [13]. - The technology commonality between automotive and robot components provides a foundation for automotive companies to enter the humanoid robot market [14]. Group 4: Market Dynamics - The collective entry of automotive companies into the humanoid robot sector is seen as a necessary response to the decline in traditional automotive narratives, with the potential for new growth stories [15][16]. - Despite the promising outlook for humanoid robots, most automotive companies have yet to generate substantial revenue in this field, with many still in the strategic planning or technology research phase [15]. - The core advantages of automotive companies in this sector include high technological commonality, automotive-grade supply chains, and large-scale manufacturing capabilities [15].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Nhat Chung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 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 针对机器人在非马尔可夫场景下缺乏对象级记忆的问题，阿肯色大学联合卡内基梅隆大学等研究团队提出LIBERO-Mem基准套件与Embodied-SlotSSM模型，通过 结构化对象记忆与时间序列建模，实现长时程、部分可观测环境下的稳健操作决策。 核心贡献 LIBERO-Mem基准：非马尔可夫机器人操作评估 设计目标 聚焦对象级部分可观测-非马尔可夫场景，通过引入对象身份、位置、关系历史的模糊性，强制模型依赖时间推理而非仅当前视觉信息。 核心特征 任务类型：包含四类任务（figure 1），覆盖不同记忆维度 现实机器人操作场景中，任务成功依赖对象交互历史（如"是否已操作过某个物体""物体之前的位置"），而非仅当前观测。 现有视觉-语言-动作模型多遵循马尔可夫假设，仅依赖即时感官输入，缺乏对象级记忆机制，在重复操作、视觉相似 ...