Core Insights - The article discusses the advancements in multimodal fusion and vision-language models (VLMs) as essential tools for enhancing robot vision technology, emphasizing their potential in complex reasoning and long-term task decision-making [4][10]. Multimodal Fusion and Robot Vision - Multimodal fusion enhances semantic scene understanding by integrating various data sources, such as visual, linguistic, depth, and lidar information, addressing limitations faced by traditional unimodal methods [8][9]. - The rise of VLMs has propelled the development of multimodal fusion paradigms, showcasing capabilities in zero-shot understanding and instruction following [9][10]. Key Applications and Challenges - The article identifies key applications of multimodal fusion in tasks like simultaneous localization and mapping (SLAM), 3D object detection, navigation, and robot manipulation [10][19]. - Challenges in multimodal fusion include cross-modal alignment, efficient training strategies, and real-time performance optimization [10][19]. Data Sets and Benchmarking - A comprehensive analysis of mainstream multimodal datasets used for robot tasks is provided, detailing their modality combinations, task coverage, and limitations [10][43]. - The importance of high-quality multimodal datasets is highlighted, as they are crucial for model training and performance evaluation [62]. Future Directions - The article suggests future research directions to address challenges in multimodal fusion, such as improving cross-modal alignment techniques and enhancing real-time performance [10][63]. - Emphasis is placed on the need for standardized datasets and benchmarks to facilitate comparisons across different research efforts [66].

Core Insights - The article discusses the potential of embodied intelligence technology to transform various devices and services, suggesting that if technical and data challenges are resolved, many everyday items could be "embodied" [1][2] - The future of the industry is expected to shift from autonomous driving to embodied intelligence over the next decade, creating numerous job opportunities and attracting talent from various fields [2][3] Group 1: Industry Trends - The emergence of humanoid robots and mobile operation robots in various sectors such as healthcare, industry, and home services is highlighted, indicating a growing trend in embodied applications [1][2] - The concept of "VLA" (Visual Language Action) in autonomous vehicles is introduced, suggesting that users will be able to interact with systems using natural language for navigation and task optimization [1][2] Group 2: Market Opportunities - The potential for service and industrial robots to perform multiple tasks in parallel is emphasized, which could lead to more efficient production lines without the need for extensive reconfiguration [2] - The retail and service industries are expected to see significant advancements with the introduction of autonomous robots, capable of managing large spaces like supermarkets and restaurants [2] Group 3: Community and Knowledge Sharing - The "Embodied Intelligence Knowledge Planet" has established a closed-loop system across various fields, including industry, academia, and job-seeking, to foster community engagement and knowledge sharing [4][5] - The platform offers resources such as technical routes, job information, and access to industry experts, aiming to support both newcomers and experienced professionals in the field [5][11][12] Group 4: Educational Resources - The community provides a comprehensive list of over 30 technical routes for learning, catering to different levels of expertise, from beginners to advanced researchers [17][12] - Various resources, including open-source projects, datasets, and simulation platforms, are compiled to facilitate learning and development in embodied intelligence [17][32][36]

Core Viewpoint - The article discusses the development of Spec-VLA, a speculative decoding framework designed to accelerate Vision-Language-Action (VLA) models, addressing challenges related to computational demands and decoding delays [3][4][16]. Research Background and Motivation - VLA models have shown significant progress in generating robot action sequences based on language instructions, but they face challenges such as the large parameter size of backbone Visual Language Models (VLMs) and increased decoding latency due to autoregressive decoding strategies [3]. - Existing acceleration methods have limitations, necessitating a tailored approach for VLA models [3]. Core Framework: Spec-VLA - Spec-VLA introduces a collaborative mechanism between draft and validation models to enhance inference speed, utilizing a draft model to predict action tokens and a validation model to ensure output quality [4][5]. Key Mechanism: Relaxed Acceptance - The relaxed acceptance mechanism allows for a defined threshold of acceptable distance between draft and validation model predictions, facilitating a more efficient decoding process without significant computational overhead [7][10]. Experimental Validation - The framework was evaluated on the LIBERO simulation benchmark across four task sets, demonstrating significant improvements in speed and acceptance length while maintaining success rates [9][10]. - The introduction of relaxed acceptance led to an acceleration factor of 1.22× to 1.42×, with acceptance length increasing by 25%-44% [10][11]. Key Results - The results indicate that as the relaxed threshold increases, the acceptance length significantly improves while maintaining stable success rates across various datasets [10][11]. - Case studies show that relaxed conditions reduce the number of iterations needed to complete action sequences, validating the effectiveness of the relaxed acceptance mechanism [13]. Conclusion and Limitations - Spec-VLA demonstrates the potential of speculative execution in VLA prediction tasks, achieving a speedup of 1.42× and a 44% increase in acceptance length without compromising success rates [16]. - Limitations include the lack of real-world robot scenario testing and the exploration of action chunking strategies [16].

01戴盟亮相华为展台，展示基于华为云平台的技术实践 在 WAIC 2025 期间，戴盟机器人精彩亮相华为展台，吸引了大量观众驻足体验。展台上，Sparky 1 瞬时响 应与无时延表现惊艳全场，展台人流如潮，成为大会期间的热门打卡点。同时，戴盟还展示了基于华为云平台 的技术实践。 作为全球首款VTLA（视觉-触觉-语言-动作）具身操作大模型，Daimon One创新性地引入触觉感知 （Tactile），突破传统VLA模型局限，通过融合视觉、触觉与语言多模态输入，直接预测动作输出，实现从 感知到控制的端到端闭环。这一突破性技术不仅显著提升了机器人在复杂场景下的推理与泛化能力，更赋予其 接近人类水平的灵巧操作智能。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 7月26日至29日，2025世界人工智能大会（下称 WAIC 2025）在上海隆重举行。作为全球人工智能领域的 顶级盛会，本次大会以"智联世界，共创未来"为主题，吸引了来自全球的顶尖企业、专家学者及行业先锋共 同参与。戴盟机器人作为具身智能技术的领先企业，携多项技术成果与创新亮相，成为本次大会的一大亮点。 尤其值得关注的是，戴盟机器人作为华为生态展台唯 ...

Core Viewpoint - The article discusses a groundbreaking research study by a team from the National University of Singapore, focusing on the VLA-OS framework, which systematically analyzes and dissects task planning and reasoning in Vision-Language-Action (VLA) models, aiming to provide insights for the next generation of general-purpose robotic VLA models [2][4]. Group 1: VLA-OS Overview - VLA-OS is a structured framework that includes a clear codebase, multimodal task planning datasets, and standardized training processes for VLA models [4][5]. - The framework aims to unify various VLA paradigms and facilitate controlled experiments to identify effective task planning representations and paradigms [19][20]. Group 2: VLA Model Paradigms - The article outlines two main approaches for integrating task reasoning into VLA models: Integrated-VLA, which combines task planning and policy learning, and Hierarchical-VLA, which separates these functions into different models [10][12]. - Current VLA models exhibit significant variability in architecture, training methods, and task planning representations, complicating performance assessments [13][15]. Group 3: Experimental Findings - The research identifies 14 key findings from over 100 experiments, highlighting the advantages of visual planning representations over language-based ones and the superior performance of Hierarchical-VLA compared to Integrated-VLA [34][35]. - Findings indicate that Integrated-VLA benefits from implicit task planning, while Hierarchical-VLA demonstrates better generalization capabilities [51][52]. Group 4: Recommendations for Future Research - The article suggests prioritizing visual representation planning and goal image planning, with language planning as a supplementary approach [68]. - It emphasizes the importance of task planning pre-training and the need for efficient training mechanisms to avoid gradient conflicts between planning and action outputs [73].

Core Viewpoint - The article discusses the unprecedented advancements in AI, particularly in embodied intelligence, which is transforming the relationship between humans and machines. This technology is poised to revolutionize various industries, including manufacturing, healthcare, and space exploration [1][3]. Group 1: Embodied Intelligence - Embodied intelligence is characterized by machines that can understand language commands, navigate complex environments, and make intelligent decisions in real-time. This technology is no longer a concept from science fiction but is rapidly becoming a reality [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in the field of embodied intelligence, focusing on creating systems that not only have a "brain" but also a "body" capable of interacting with the physical world [1][3]. Group 2: Technical Challenges - Achieving true embodied intelligence presents significant technical challenges, including the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [3][4]. - MuJoCo (Multi-Joint dynamics with Contact) is highlighted as a critical technology in this field, serving as a high-fidelity simulation engine that bridges the virtual and real worlds [4][6]. Group 3: Advantages of MuJoCo - MuJoCo allows researchers to create realistic virtual robots and environments, enabling millions of trials and learning experiences without risking expensive hardware. This significantly accelerates the learning process, as simulations can run hundreds of times faster than real-time [6][8]. - The technology supports high parallelism, allowing thousands of simulation instances to run simultaneously, and provides a variety of sensor models, ensuring robust and precise simulations [6][8]. Group 4: Educational Opportunities - A comprehensive MuJoCo development course has been developed, focusing on practical applications and theoretical foundations, covering topics from physical simulation principles to deep reinforcement learning [9][11]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of embodied intelligence technologies [15][17]. Group 5: Project-Based Learning - The course includes six progressively challenging projects, such as building a smart robotic arm, implementing vision-guided grasping systems, and developing multi-robot collaboration systems, which are designed to provide hands-on experience [19][27]. - Each project is accompanied by detailed documentation and code references, facilitating a deep understanding of the underlying technologies and their applications in real-world scenarios [30][32]. Group 6: Target Audience and Outcomes - The course is suitable for individuals with programming or algorithm backgrounds looking to enter the field of embodied robotics, as well as students and professionals interested in enhancing their practical skills [32][33]. - Upon completion, participants will possess a complete skill set in embodied intelligence, including technical, engineering, and innovative capabilities, making them well-equipped for roles in this rapidly evolving industry [32][33].

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。 本文的作者团队来自北京大学和银河通用机器人公司。第一作者为北京大学计算机学院前沿计算研究中心博士生吕江燃，主要研究方向为具身智能，聚焦于世 界模型和机器人的灵巧操作，论文发表于 ICCV，TPAMI，RSS，CoRL，RAL 等机器人顶会顶刊。本文的通讯作者为北京大学计算机学院教授王亦洲和 北京 大学助理教授、银河通用创始人及CTO 王鹤。 尽管当前的机器人视觉语言操作模型（VLA）展现出一定的泛化能力，但其操作模式仍以准静态的抓取与放置（pick-and-place）为主。相比之下，人类在操作物 体时常常采用推动、翻转等更加灵活的方式。若机器人仅掌握抓取，将难以应对现实环境中的复杂任务。例如，抓起一张薄薄的银行卡，通常需要先将其推到桌 边；而抓取一个宽大的盒子，则往往需要先将其翻转立起（如图 1 所示）： 这些技能都属于一个重要的领域：非抓握操作（Non-prehensile Manipulation） ...

我们邀请具身领域的大佬，为行业开创具身教育在线课程、企业咨询、辅导业务。 如果您是大模型/多模态大模型、Diffusion、VLA、VLA+RL、sim2real、端到端、具身交互、视觉语言导航、 强化学习、机器人运动规划、抓取与位姿估计、触觉感知、大模型部署与量化感知推理、机器人仿真等方向， 欢迎加入我们一起为行业输出最优秀的教程。 我们期望您是博士（包括在读）及以上学历，工业界希望能有2年以上的研发经验。 最近陆续在和国内外的具身团队接触，发现前面存在的一些问题也被慢慢突破了。很开心看到具身智能这个领 域发展的这么迅速，有几家明星公司也陆续开始准备上市，在这个过程中能够服务好大家和行业是我们一直坚 持的。 越是做平台越是发现，产业离不开大家的共同努力，特别是早期。技术的孤立和闭塞虽然能产生一定的技术壁 垒，但对整个产业的发展并不友好。我们一直鼓励大家能够积极交流，也期望自身能够承担一个汇聚全行业人 才的平台。刚发布具身一周年的推文，1周年后我们期望能够邀请更多有力量的大佬加入我们，一起推动行业 的进步。 现具身智能之心邀请全球具身领域开发研究者，一起和我们参与具身项目合作、具身教育研发。 具身项目合作 我 ...

Core Viewpoint - The article discusses the evolution of robot navigation technology from traditional mapping and localization to large model-based navigation, which includes visual language navigation (VLN) and goal navigation. VLN focuses on following instructions, while goal navigation emphasizes autonomous exploration and pathfinding based on environmental understanding [1][5]. Group 1: Visual Language Navigation (VLN) - VLN is fundamentally a task of following instructions, which involves understanding language commands, perceiving the environment, and planning movement strategies. The VLN robot system consists of a visual language encoder, historical environmental representation, and action strategy modules [2][4]. - The learning process for the strategy network has shifted from extracting patterns from labeled datasets to leveraging large language models (LLMs) for effective planning information extraction [4] - The architecture of VLN robots requires them to accumulate visual observations and execute actions in a loop, making it crucial to determine the current task stage for informed decision-making [4]. Group 2: Goal Navigation - Goal navigation extends VLN by enabling agents to autonomously explore and plan paths in unfamiliar 3D environments based solely on target descriptions, such as coordinates or images [5][7]. - Unlike traditional VLN, goal-driven navigation systems must transition from understanding commands to independently interpreting the environment and making decisions, integrating computer vision, reinforcement learning, and 3D semantic understanding [7]. Group 3: Commercial Applications and Demand - Goal-driven navigation technology has been successfully implemented in various verticals, such as terminal delivery, where it combines with social navigation algorithms to handle dynamic environments and human interactions [9]. - Companies like Meituan and Starship Technologies have deployed delivery robots in complex urban settings, while others like Aethon have developed service robots for medical and hospitality sectors, enhancing service efficiency [9][10]. - The growth of humanoid robots has led to an increased focus on adapting navigation technology for applications in home services, healthcare, and industrial logistics, creating significant job demand in the navigation sector [10]. Group 4: Learning and Knowledge Challenges - Both VLN and goal navigation require knowledge across multiple domains, including natural language processing, computer vision, reinforcement learning, and graph neural networks, making it challenging for newcomers to gain comprehensive expertise [11]. - The fragmented nature of knowledge in these fields can lead to difficulties in learning, often causing individuals to abandon their studies before achieving a solid understanding [11].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 岗位职责描述 社招&校招&实习生都需要，欢迎投递简历到pwwang@baai.ac.cn 职位要求 投递说明 1. 计算机科学、人工智能、机器人、自动化、数学等相关领域的硕士及以上学历； 2. 精通 Python，具有良好的深度学习基础，熟悉 TensorFlow、PyTorch 等深度学习框架； 3. 具备大模型领域的研究经验，对主流视觉与语言大模型有深入理解，具备预训练、微调、部署等流程的工 作经验； 4. 具备机器人控制经验，对主流具身模型训练以及部署有良好的经验优先 5. 具备优秀的学习能力，英语水平，动手能力以及良好的团队沟通与协作能力； 6. 有相关机器人、自然语言处理以及计算机视觉顶会论文（RSS，ICRA, CVPR, CoRL, ICLR, NeurlPS,ACL 等）发表优先。 1. 负责具身智能大模型（VLA大模型或者分层架构）的研究和开发。 2. 设计，优化模型架构，完成对模型的数据处理，训练与真机部署工作。 3. 深入调研具身智能领域相关的前沿技术，跟踪业内大模型领域的最新进展并推进相关研究，探寻将最新技 术应用到具身智能领域的可能 ...