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].

Core Viewpoint - Google DeepMind has launched the Gemini Robotics 1.5 series, marking a significant advancement in robotics with the introduction of embodied reasoning capabilities, enabling robots to think before acting and perform complex tasks [3][5][10]. Group 1: Model Composition - The Gemini Robotics 1.5 series consists of two models: GR 1.5 for action execution and GR-ER 1.5 for enhanced reasoning capabilities [6][4]. - GR 1.5 is designed for executing multimodal tasks, while GR-ER 1.5 focuses on planning and understanding [6][23]. Group 2: Task Execution and Adaptability - The combination of GR 1.5 and GR-ER 1.5 allows robots to perform multi-step tasks, such as sorting clothes or packing luggage based on weather conditions [7][8][20]. - The models enable zero-shot cross-platform capability, allowing skills learned on one robot to be transferred to another without additional training [9][19]. Group 3: Reasoning and Planning - GR-ER 1.5 generates an internal dialogue to break down complex tasks into smaller steps before execution, enhancing robustness and interpretability [25][50]. - The models can self-correct during task execution, improving efficiency and safety in human environments [31][32]. Group 4: Motion Transfer Mechanism - The innovative Motion Transfer mechanism allows different robot platforms to share skills by mapping their motion trajectories to a unified action semantic space [46][52]. - This mechanism enhances task generalization and cross-robot migration capabilities, allowing robots to adapt to new environments effectively [47][53]. Group 5: Performance and Safety - In benchmark tests, GR 1.5 outperformed previous models in instruction generalization, action generalization, and task completion rates, achieving nearly 80% in long-sequence tasks [58][59]. - The models maintain high safety standards, demonstrating superior risk recognition and intervention capabilities, ensuring safe operation in human environments [61][62].

Core Insights - The article discusses the development of SimpleVLA-RL, a new framework designed to enhance the training and generalization capabilities of Visual-Language-Action (VLA) models in robotics, addressing key limitations in existing training paradigms [4][14]. Group 1: Key Contributions of SimpleVLA-RL - SimpleVLA-RL effectively addresses three major bottlenecks in VLA model training: high data collection costs, insufficient generalization ability, and the need for large-scale demonstration data [6][11]. - The framework demonstrates state-of-the-art (SoTA) performance in standard benchmarks such as LIBERO and RoboTwin, achieving significant improvements in success rates even with limited data [6][21]. - In scenarios with single demonstration data, the average success rate of OpenVLA-OFT in LIBERO increased from 48.9% to 96.9%, and for long-sequence tasks, it improved from 17.3% to 91.7% [6][21]. Group 2: Training Mechanism and Innovations - The training mechanism includes interactive trajectory sampling, result reward modeling, and exploration enhancement, which collectively improve data efficiency and model performance [15][16][17]. - The result reward model simplifies the reward structure to binary outcomes (success or failure), allowing for better focus on training objectives and avoiding the complexities of process rewards [16][21]. - The exploration enhancement strategy encourages diverse exploration during training, preventing the model from converging to narrow solutions [17][19]. Group 3: Performance Metrics and Benchmark Results - SimpleVLA-RL achieved an average success rate of 99.1% in the LIBERO benchmark, with specific improvements in long-sequence tasks, where success rates increased by 12.0 percentage points [23]. - In RoboTwin1.0, the average success rate improved from 39.8% to 70.4%, with notable gains in specific tasks such as "Blocks Stack," which saw a 33.1 percentage point increase [25]. - The framework also demonstrated significant performance improvements in RoboTwin2.0, with average success rates rising from 38.3% to 68.8%, surpassing previous models [27]. Group 4: Real-World Application and Generalization - The model trained solely on simulation data showed enhanced adaptability to real-world tasks, with average success rates increasing from 17.5% to 38.5% in practical applications [30]. - The emergence of the "Pushcut" phenomenon indicates that the model can autonomously discover new strategies beyond human demonstrations, showcasing its potential for adaptive learning [32][34].

Group 1 - The article promotes a series of discounts and offers related to embodied intelligence courses and services, running from September 24 to October 12 [1][4][6] - New members joining the knowledge community can enjoy a 30% discount, while existing members can renew at a 50% discount [1][4] - Various courses, including VLA, VLN, and Diffusion Policy, are available at a 20% discount [2] Group 2 - The Super Discount Card offers a 30% discount on all courses for one year [4][7] - One-on-one paper tutoring can provide a maximum discount of 5,000 yuan for a 1,000 yuan fee, while group tutoring (1v6) offers a 1,000 yuan reduction [4][7] - The article highlights various research hardware available, including reinforcement learning platforms and robotic arms [4][7]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Wei Gao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。 一、核心背景与问题提出 机器人端到端操作策略为实体智能体理解和交互世界提供了巨大潜力。与传统模块化流水线不同，端到端学习能缓解模块间信息损失、孤立优化目标导致的特征 错位等关键局限，但现有端到端神经网络（包括基于大视觉-语言-动作（VLA）模型的方法），在大规模实际部署中性能仍显不足——尤其是在可靠性、精度 上，甚至逊色于工程化成熟的传统模块化流水线，且在面对未见过的物体或不同机器人平台时，泛化能力短板更突出。 为填补"泛化潜力"与"实际性能需求"的差距，本研究提出一种以"可用性（affordance）"为核心的端到端机器人操作方案：将可用性定义为"任务相关、语义明确 的物体局部区域"，并通过"任务特定的定向关键点"来具象化这一概念，最终形成"移动定向关键点链（Chain of Moving Oriented Keypoi ...