Core Viewpoint - ROSCon China 2025 will be held from October 31 to November 1, 2025, at the Shanghai Hongqiao Xinhua Union Sofitel Hotel, promising a valuable and content-rich event for attendees [2]. Group 1: Event Highlights - The previous ROSCon China 2024 featured industry leaders sharing insights on cutting-edge algorithms and practical case studies, creating an engaging atmosphere for discussions [3]. - Attendees had opportunities to interact with experts and network with peers, fostering collaboration and idea exchange within the robotics community [4]. - The event is designed to showcase the latest advancements in robotics technology, allowing participants to experience innovative products firsthand [12][16]. Group 2: Participation Opportunities - The conference is currently accepting proposals for guest speeches, workshops, and lightning talks, focusing on ROS1 and ROS2 topics [13][14]. - Interested speakers can submit their topics and personal/team profiles to share their insights on the conference stage [16]. - The event aims to gather top experts globally to discuss the latest research and applications in robotics, helping attendees stay updated on industry trends [16]. Group 3: Sponsorship and Ticketing - The conference is actively recruiting sponsors, providing brands with exposure and collaboration opportunities within the robotics sector [17]. - Early bird tickets are available for purchase, with limited quantities, encouraging prompt registration for the event [20]. - Attendees can enjoy discounted hotel rates at the conference venue, enhancing the overall experience [22].

许多自驾和传统机器人公司，已经开始成立具身实验室了...... 今天在和朋友喝下午茶，聊到了很多公司开始筹建具身团队和业务线。其中不乏有自动驾驶公司、 主机厂、新势力、传统机器人公司、传统臂商。 貌似又到了那个谁不入场就会被时代抛弃的时候，先不妨看看这几类公司的出发点吧。对于自驾公 司和主机厂，他们想做的更多是解决工厂智能部分，造车需要大量的工人，包括制造、搬运、特殊 场景的需求。如果机器人能够在固定场景下完成相对智能，那么能省下不少成本。 对于传统机器人公司来说，比如扫地机器人公司，他们更希望升级已有产品，具备更加智能的服 务。比如扫地机上添加机械臂，用户可以下达指令完成清扫之外的工作以及通过大模型实现更好的 交互功能。传统臂商，也想进一步参与臂的智能化升级，适配更加泛化和多样的场景。 当然，也不排除很多公司通过投资新兴产业来赚取更多的利润。但这个趋势会导致，很多公司的资 源倾斜到具身领域。无论是数据生产模块还是算法、本体层面。很多公司的具身岗位存在缺口，一 些没有经验的leader不得不扛起大旗！持续的面试仍然招不到合适的，因为真的懂的人很少。 归根到底是没有系统的培养体系，导致这方面的人才出现了数量和质 ...

但随着研究不断深入，行业对于数据提出了更高要求： 更高 的 物理精度 ，以保证数据与现实世界的贴合度； 更丰富的交互类型 ，覆盖刚体、软体、流体等复杂 场景； 更强的扩展性与稳定性 ，既支持科研中的微观动力学细节，也能满足产业应用的大规模仿真需求。 在这样的背景下，RoboScience 推出了面向具身智能的高精度通用物理仿真平台 「 RoboMirage 」。 「RoboMirage」具有以下核心特性： 作者丨机器之心 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。 在具身智能的发展路径中，如何获得海量且高质量的数据是行业绕不开的核心问题。 如果说大语言模型依赖于互联网规模的语料库，那么具身智能的成长同样需要规模化的交互经验。现实中，收集这些数据的代价极高：机械臂等硬件部署成本 高，单台投入就需数万元，且难以规模化；数据采集环节依赖经验丰富的数采员且耗时漫长。而在仿真环境中，智能体则可以以更低成本、更高效率进行无限次 试错，从而快速积累大规模交互经验 ...

Core Insights - The article discusses the evolution and challenges of embodied intelligence, emphasizing the need for a comprehensive understanding of its development, issues faced, and future directions [3][4]. Group 1: Robotic Manipulation - The survey on robotic manipulation highlights the transition from mechanical programming to embodied intelligence, focusing on the evolution from simple grippers to dexterous multi-fingered hands [5][6]. - Key challenges in dexterous manipulation include data collection methods such as simulation, human demonstration, and teleoperation, as well as skill learning frameworks like imitation learning and reinforcement learning [5][6]. Group 2: Navigation and Manipulation - The discussion on robotic navigation emphasizes the importance of physics simulators in addressing high costs and data scarcity in real-world training, with a focus on the Sim-to-Real transfer challenges [9][15]. - The evolution of navigation techniques is outlined, transitioning from explicit memory to implicit memory, and the role of various simulators in narrowing the Sim-to-Real gap is analyzed [15][16]. Group 3: Multimodal Large Models - The exploration of embodied multimodal large models (EMLMs) reveals their potential to bridge perception, cognition, and action gaps, driven by advancements in large model technologies [17][19]. - Challenges identified include cross-modal alignment difficulties, high computational resource demands, and weak domain generalization [19]. Group 4: Teleoperation and Data Collection - The survey on teleoperation of humanoid robots discusses the integration of human cognition with robotic capabilities, particularly in hazardous environments, while addressing challenges such as high degrees of freedom and communication limitations [29][30]. - Key components of teleoperation systems include human state measurement, motion retargeting, and multimodal feedback mechanisms [30][33]. Group 5: Vision-Language-Action Models - The analysis of Vision-Language-Action (VLA) models covers their evolution from cross-modal learning architectures to the integration of visual language models and action planners [33][36]. - The article identifies core challenges in real-time control, multimodal action representation, and system scalability, while proposing future directions for adaptive AI and cross-entity generalization [36][41].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 >>直播和内容获取转到 → 具身智能之心知识星球 点击按钮预约直播 热身材料 ： 分享介绍 美的具身基座模型负责人 ...... 2. 拓展VLA模型能力边界 3. 提升VLA模型泛化能力 1. DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control. 2. ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge. 3. ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model. 4. Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reason ...

添加小助理微信AIDriver005，备注昵称+机构+遥操加群，可以第一时间进群。 具身智能之心遥操作技术交流群来了！欢迎相关方向的同学加入一起交流。 ...

Group 1 - The core viewpoint of the article highlights the upcoming IPO of Yushu Technology, which is scheduled to submit its application between October and December 2025, marking a significant milestone for the company and the embodied robotics industry [1] - The recognition of embodied robotics by the market and capital is seen as a positive development, suggesting that subsequent IPOs in this sector are likely to follow, expanding the market's potential and driving the growth of related industries [1] - The article emphasizes that the embodied intelligence field is still in its growth phase, presenting a promising research direction and career advancement opportunities for those in the industrial sector [3] Group 2 - The article promotes various learning resources and research platforms provided by the company for those interested in entering the embodied intelligence field, indicating a strong commitment to education and professional development [3] - A special discount card for courses is introduced, offering a 30% discount on all embodied courses for students purchasing two or more courses, valid for one year [4] - The company’s knowledge community is highlighted as the largest in the domestic market, facilitating communication among nearly 2,000 members [7]

Group 1 - The article introduces a new communication group focused on remote operation technology related to embodied intelligence, inviting individuals interested in this field to join for discussions [1] - The group aims to facilitate knowledge sharing and collaboration among professionals and students in the relevant sectors [1] Group 2 - Interested individuals can join the group by adding a designated assistant on WeChat, providing their nickname, institution, and a request to join the remote operation group [2]

Core Viewpoint - The exploration of Artificial General Intelligence (AGI) is increasingly focusing on embodied intelligence, which emphasizes the interaction and adaptation of intelligent agents within physical environments, enabling them to perceive, understand tasks, execute actions, and learn from feedback [1][3]. 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 brain and cerebellum technologies [3]. - Major domestic companies like Huawei, JD.com, Tencent, Ant Group, and Xiaomi are actively investing and collaborating to build key technologies in embodied intelligence, while international players like Tesla and investment firms are supporting companies like Wayve and Apptronik in autonomous driving and warehouse robotics [5]. Technological Evolution - The development of embodied intelligence has progressed through several stages: - The first stage focused on grasp pose detection, which lacked the ability to model task context and action sequences, limiting its effectiveness in complex operations [6]. - The second stage involved behavior cloning, allowing robots to learn from expert demonstrations but revealing weaknesses in generalization and performance in multi-target scenarios [6]. - The third stage introduced Diffusion Policy methods, enhancing stability and generalization by modeling action trajectories, followed by the emergence of Vision-Language-Action (VLA) models that integrate visual perception, language understanding, and action generation [7][9]. - The fourth stage, starting in 2025, explores the integration of VLA models with reinforcement learning, world models, and tactile sensing to overcome current limitations [9][11][12]. Product and Market Development - The evolution of embodied intelligence technologies has led to the emergence of various products, including humanoid robots, robotic arms, and quadrupedal robots, serving industries such as manufacturing, home services, dining, and healthcare [14]. - The demand for engineering and system capabilities is increasing as the industry shifts from research to deployment, necessitating higher engineering skills for effective implementation [17].

Core Viewpoint - The article discusses RoboMemory, a brain-inspired multi-memory framework designed for lifelong learning in physical embodied systems, addressing key challenges in dynamic real-world environments [2][4]. Group 1: Framework Overview - RoboMemory is designed to tackle four core challenges: continuous learning capability, multi-module memory latency, task relevance capture, and avoidance of deadlock in closed-loop planning [2]. - The framework integrates four core modules: information preprocessing system (thalamus-like function), lifelong embodied memory system (hippocampus-like function), closed-loop planning module (prefrontal cortex-like function), and low-level executors (cerebellum-like function) [2]. Group 2: Memory System Features - The lifelong embodied memory system features parallel updating and retrieval mechanisms across four sub-modules: spatial memory, temporal memory, episodic memory, and semantic memory, effectively resolving reasoning speed bottlenecks in complex memory architectures [2]. - The system employs dynamic knowledge graphs and a consistent architecture design, significantly enhancing memory coherence and scalability [2]. Group 3: Application and Impact - The article emphasizes the importance of memory systems for embodied agents in real-world environments, highlighting the need for continuous learning capabilities [4][6]. - The discussion includes the pain points faced by embodied agents in real environments and how a robust memory system can address these challenges [6].