Core Viewpoint - The article highlights the increasing number of companies in the embodied intelligence sector in China, nearing 200, indicating a potential for market saturation and competition [1]. Group 1: Industry Landscape - The number of companies in the embodied intelligence field, including robotics and internet companies, is approaching 200, leading to high similarity in business and product offerings [1]. - Companies are adopting different strategies, with some focusing on integrating applications while others prioritize core research and development, aiming for long-term sustainability [1]. Group 2: Community and Support - The "Embodied Intelligence Knowledge Planet" community aims to create a large platform for both beginners and advanced learners in the field, providing job referrals and academic guidance [3]. - The community has established a closed loop across various domains, including industry, academia, and job exchanges, facilitating problem-solving and knowledge sharing [5]. Group 3: Educational Resources - The community has compiled over 30 technical routes for newcomers, significantly reducing the time needed for research and learning [6]. - Various resources, including open-source projects, datasets, and technical learning paths, are available to assist individuals at different stages of their careers [15][32]. Group 4: Networking and Collaboration - The community connects members with industry leaders and provides opportunities for collaboration through forums and live discussions on various topics related to embodied intelligence [6][21]. - Members can freely ask questions and receive guidance on career choices and research directions, fostering a supportive environment for professional growth [75].

Core Viewpoint - The company "Embodied Intelligence Heart" is a leading creative platform in the domestic embodied intelligence field, dedicated to promoting the development of the embodied industry and talent cultivation [1]. Group 1: Industry Development - The company emphasizes the importance of continuous progress in both industry and academia, inviting experts from both fields to collaborate and create professional and in-depth work for the benefit of the entire industry [1]. Group 2: Content Creation - The main content produced includes sharing of the latest technologies and papers, explanations of core technology modules, industry analysis articles, and in-depth technical stack sharing [2]. - The company offers financial support for contributions and personal IP support, encouraging individuals to join their community and share industry resources [2].

Core Insights - The article emphasizes the importance of building a solid foundation in research before diving into complex topics like VLA (Vision-Language-Action) in embodied intelligence [1][6] - VLA is highlighted as a significant research area that breaks traditional single-task limitations, allowing robots to make autonomous decisions in diverse environments [4][6] - The article discusses the rapid development of the embodied intelligence sector, with various teams transitioning from laboratory research to commercialization, supported by major tech companies [6] Summary by Sections VLA Overview - VLA enables the execution of commands through language, facilitating continuous actions and enhancing user experience [2] - It represents a shift from traditional methods, allowing for multi-tasking capabilities in robots across various applications [4] Industry Development - The embodied intelligence field is experiencing robust growth, with companies like Unitree and tech giants like Huawei and Tencent actively investing in this area [6] - The collaboration between academia and industry is being fostered through various projects and research initiatives [4][6] Educational Initiatives - A specialized course on VLA research is being offered to help students navigate the complexities of the field, focusing on practical skills and research methodologies [10][12] - The course aims to equip students with the ability to identify research opportunities, design experiments, and write academic papers [12][16] Learning Outcomes - Students completing the course will gain comprehensive knowledge of VLA models, experimental design, and the academic writing process [16] - The program emphasizes the development of independent research capabilities and the ability to produce a complete research paper [16]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨 具身智能之心 专家学习 —— 首先在全数据上训练一个基础控制策略，作为专家模型的初始点。随后，针对聚类结果在各动作簇上分别微调，得到更具针对性的专家模型。接 着，将专家模型部署到真实机器人上执行以采集轨迹，并基于这些轨迹为每个类别单独训练动作增量模型，再冻结增量模型对专家进行微调，实现对仿真与现实间 偏差的补偿。通过迭代更新，专家模型在"更优策略—更高质量数据—更精准增量—再优化专家"的循环中逐步提升性能。 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。 BumbleBee 提出了一条完整的人形机器人全身控制训练流程。首先，利用 AMASS 数据集训练基础的全身控制模型；在此基础上，通过聚类区分不同类型的动作， 并分别训练相应的专家控制模型；随后，将这些专家模型部署到真实机器人上，采集执行轨迹；基于采集的轨迹序列，为每个专家模型训练对应的动作增量模型 （delta model），以缓解仿真与现实之间的差距（ ...

Core Background and Objectives - The current mobile operation technology is expanding from closed, structured work units to open, unstructured large indoor environments, requiring robots to explore unfamiliar and cluttered spaces, interact with diverse objects and humans, and respond to natural language commands for tasks such as home service, retail automation, and warehousing logistics [3] - AnywhereVLA proposes a modular architecture that integrates the robustness of classical navigation with the semantic understanding capabilities of VLA models to achieve language-driven pick-and-place tasks in unknown large indoor environments, capable of real-time operation on consumer-grade hardware [3] Review of Existing Solutions: Advantages and Limitations - VLA models and lightweight optimization strategies are discussed, highlighting their limitations in spatial perception and adaptability to large environments [4] - Existing solutions like MoManipVLA and SmolVLA show performance close to larger models while reducing resource requirements, but they lack spatial awareness for large environments [4] - The limitations of visual-language navigation (VLN) and classical navigation frameworks are outlined, emphasizing the need for improved language understanding and semantic reasoning capabilities [4] AnywhereVLA Architecture: Four Core Modules and Workflow - The AnywhereVLA architecture processes natural language commands through four modules to output low-level control instructions for driving base wheels and robotic arm joints [4] - The workflow includes language instruction parsing, guiding VLA operations, constructing 3D semantic maps, and executing operations based on the identified targets [7] VLA Model Fine-tuning and Hardware Platform - The SmolVLA model is fine-tuned to enhance its operational capabilities, with specific input data and key steps outlined for optimizing performance [13][15] - The HermesBot mobile operation platform is designed specifically for AnywhereVLA, balancing sensing and computational capabilities [16] Experimental Results: Performance and Effectiveness Validation - In an unknown multi-room laboratory setting, 50 pick-and-place tasks were executed, with a core success rate of 46%, and the fine-tuned SmolVLA operation module achieving an 85% success rate [17][22] - The performance metrics for various modules are provided, indicating robust SLAM performance and varying success rates for active environment exploration, navigation, object detection, and VLA manipulation [22] - Time efficiency metrics show that the average task completion time is under 133 seconds for a 5m exploration radius, meeting real-time scene requirements [23]

面向具身科研领域打造的轻量级高性价比机械臂 还在为具身领域的硬件发愁吗？太贵的硬件买不起，太便宜的机械臂不好用，有没有一款价格低但质量很 高的产品？ Imeta-y1来了！低成本可以完成具身领域论文的验证，科研场景的开发，满足大多数从业人员和科研工作者 的需求。 这是一款专为教育、科研与轻工业场景设计的轻量级机械臂。 该机械臂融合高精度运动控制、低功耗设计与开放软硬件架构，支持从仿真到真机的无缝联调，并提供全 流程开源SDK与工具链，助力用户快速实现算法验证、数据采集、模型训练与部署应用。 其紧凑型结构与模块化接口，尤其适用于嵌入式AI与机器人学习平台的开发与应用推广。 | 本体重量 | 4.2KG | 额定负载 | 3KG | 自由度 | 6 | | --- | --- | --- | --- | --- | --- | | 工作半径 | 612.5mm | 重复定位精度 | ±0. 1mm | 底座安装尺寸 | 90mm*90mm*M5*4 | | 供电电压 | 24V | 控制器 | PC | 材质 | 铝合金 | | 通讯方式 | CAN | 外部接口 | 电源+CAN XT30 2+2 | 控制方式 ...

面向具身科研领域打造的轻量级高性价比机械臂 还在为具身领域的硬件发愁吗？太贵的硬件买不起，太便宜的机械臂不好用，有没有一款价格低但质量很高的 产品？ Imeta-y1来了！低成本可以完成具身领域论文的验证，科研场景的开发，满足大多数从业人员和科研工作者的 需求。 这是一款专为教育、科研与轻工业场景设计的轻量级机械臂。 该机械臂融合高精度运动控制、低功耗设计与开放软硬件架构，支持从仿真到真机的无缝联调，并提供全流程 开源SDK与工具链，助力用户快速实现算法验证、数据采集、模型训练与部署应用。 其紧凑型结构与模块化接口，尤其适用于嵌入式AI与机器人学习平台的开发与应用推广。 | 本体重量 | 4.2KG | 额定负载 | 3KG | 自由度 | 6 | | --- | --- | --- | --- | --- | --- | | 工作半径 | 612.5mm | 重复定位精度 | ±0. 1mm | 底座安装尺寸 | 90mm*90mm*M5*4 | | 供电电压 | 24V | 控制器 | PC | 材质 | 铝合金 | | 通讯方式 | CAN | 外部接口 | 电源+CAN XT30 2+2 | 控制方式 ...

Core Insights - The article emphasizes the importance of building a solid foundation in research before diving into complex topics like VLA (Vision-Language-Action) in embodied intelligence [1][6] - VLA is highlighted as a transformative model that allows robots to perform tasks based on language instructions, breaking the limitations of traditional single-task training [4][7] - The article discusses the rapid development of the embodied intelligence sector, with various teams transitioning from research to commercialization, and major tech companies actively investing in this field [6] Summary by Sections VLA Overview - VLA enables robots to autonomously make decisions in diverse environments, significantly enhancing their adaptability and application across industries such as manufacturing and logistics [4][6] - The model has become a research hotspot, fostering collaboration between academia and industry through various projects like pi0, RT-2, and OpenVLA [4][7] Industry Development - The embodied intelligence field is experiencing robust growth, with companies like Unitree, Zhiyuan, and major tech players like Huawei and Tencent making significant strides [6] - There is a growing interest in VLA-related research, with many seeking guidance to quickly enter or transition within this domain [6] Course Offerings - A specialized course on VLA research is introduced, focusing on the theoretical and practical aspects of embodied intelligence, including simulation environment setup and experimental design [10][12] - The course aims to cultivate independent research capabilities, guiding students from idea generation to the completion of a research paper [12][17] Learning Outcomes - Participants will gain comprehensive knowledge of VLA models, practical experience in simulation, and skills in academic writing and research methodology [17] - The course is designed to help students identify research opportunities and navigate the complexities of the embodied intelligence landscape [12][16]

Group 1 - The article emphasizes the importance of community engagement and the development of hardware solutions to address user complaints about expensive and inefficient products [2][3] - The community aims to create a comprehensive platform for knowledge sharing in the field of embodied intelligence, including job referrals and academic guidance [5][12] - The community has established connections with numerous universities and companies in the embodied intelligence sector, facilitating collaboration and resource sharing [13][19] Group 2 - The community has compiled over 30 technical routes and invited industry experts to provide insights and answer questions related to embodied intelligence [6][10] - Various forums and live sessions are organized to discuss advancements in the field, covering topics such as robot simulation, data collection, and decision-making frameworks [6][17] - The community offers a wealth of resources, including open-source projects, datasets, and educational materials to support both beginners and advanced researchers [29][35][39] Group 3 - The community provides a structured learning path for newcomers, including technical stacks and routes for different areas of embodied intelligence [8][14] - For those already engaged in research, valuable industry frameworks and project proposals are available to enhance their work [10][12] - The community fosters a collaborative environment where members can freely ask questions and receive guidance on career and research direction [73][80]

Core Viewpoint - Neural Rendering (NERF/3DGS) is revolutionizing 3D reconstruction technology, significantly enhancing the realism of images used in autonomous driving and embodied intelligence simulations, addressing the limitations of traditional computer graphics rendering [3][4]. Group 1: Background and Technology - NERF and 3DGS utilize neural networks to express spatial data, excelling in new perspective synthesis, which is crucial for sensor simulation in autonomous driving and embodied intelligence [3]. - The integration of NERF and 3DGS into existing simulation frameworks is proposed as a more efficient approach than developing new frameworks from scratch, allowing for real-time rendering while leveraging existing 3D digital assets and algorithm interfaces [3][4]. Group 2: Implementation in Simulation Software - NVIDIA's Isaac Sim has incorporated neural rendering technology, enabling the insertion of 3DGS models into simulation environments, allowing for both static backgrounds and dynamic interactive objects [4][5]. - The process of importing 3DGS models into Isaac Sim involves generating USDZ models and ensuring they possess physical properties for interaction within the simulation [5][8]. Group 3: Model Interaction and Physics - To achieve realistic interactions, imported models must have physical attributes added, such as collision properties, to ensure they interact correctly with other objects in the simulation [8][14]. - The integration of dynamic objects, such as a LEGO bulldozer, into the simulation environment demonstrates the capability of 3DGS models to interact with both static and dynamic elements [11][15]. Group 4: Performance and Future Considerations - The performance metrics indicate that even with a high workload, the simulation maintains a good frame rate and low memory usage, showcasing the efficiency of the neural rendering technology [17]. - Future challenges include improving light and shadow interactions between 3DGS models, providing accurate ground truth information for algorithms, and enhancing computational efficiency for larger scenes [18][19].