点击下方 卡片 ，关注" 具身智能 之心 "公众号 一、你的3D Grounding 模型真的work吗？ 在具身智能系统中，智能体需要依靠第一视角的3D感知算法来理解周边环境。作为其中的核心任务之一，Embodied 3D Grounding是指根据ego-centric的RGB- D图像序列以及语言描述在三维空间中定位目标对象，要求模型能够融合语言与三维视觉信息，准确识别出语句中所指代的物体。当前主流方法多采用两阶段策 略，即先利用检测模型提取三维区域特征，再进行语言引导的grounding微调。这自然引出一个疑问： 第二阶段这种针对 Grounding 的微调，其效果究竟如何，它 真的work吗？ 令人颇感意外的是，实证结果显示，即便是当前最先进的Grounding模型，其实际表现也远未达到预期。相反，那些完全未接受语言监督、仅依赖目标类别进行筛 选的检测模型，在Grounding任务的评估中竟取得了更优的结果。具体而言，考虑到任务中的语言指令为模板生成，本文通过规则解析提取出目标物体的类别标 签，之后使用该类别从检测模型中筛选对应预测框，直接作为Grounding的输出。理论上，这种做法缺乏语言理解过 ...

Core Insights - The article highlights the growth and development of the embodied intelligence community, aiming to expand to a scale of 2000 members, showcasing various projects and initiatives in the field [1][5]. Group 1: Community Development - The community has witnessed significant advancements with the introduction of various projects such as ACT, RDT-1/RDT-2, CogACT, OpenVLA, π0, and π0.5 [1]. - A total of over 30 technical routes have been organized internally to assist members in finding benchmarks, reviews, and learning pathways, significantly reducing search time [1]. - The community has invited numerous industry experts to engage with members, providing opportunities for Q&A sessions and discussions on the latest developments in embodied intelligence [1]. Group 2: Job Opportunities and Networking - The community has established a job referral mechanism with multiple embodied intelligence companies, facilitating members in submitting their resumes to desired companies [2]. - Members are encouraged to join the community to connect with nearly 200 companies and institutions in the embodied intelligence sector, fostering collaboration and knowledge sharing [5]. Group 3: Educational Resources - The community has compiled a wealth of resources for newcomers, including over 40 open-source projects and nearly 60 datasets related to embodied intelligence [11]. - Various learning paths have been outlined, covering topics such as reinforcement learning, multi-modal large models, and robot navigation, catering to both beginners and advanced members [11][12]. - Regular discussions and sharing sessions are held to address common questions in the field, such as robot simulation platforms and imitation learning for humanoid robots [12]. Group 4: Industry Insights - The community provides a comprehensive overview of domestic and international embodied intelligence companies, covering various sectors such as education, logistics, and healthcare [17]. - Members have access to a collection of industry reports and academic papers, enabling them to stay updated on the latest trends and applications in embodied intelligence [19]. - The community also offers insights into the latest advancements in robotics, including navigation, planning, and multi-modal model integration [41][49].

Core Insights - The article provides a comprehensive overview of various surveys and research papers related to embodied intelligence, focusing on areas such as vision-language-action models, reinforcement learning, and robotics applications [1][2][3][4][5][6][8][9] Group 1: Vision-Language-Action Models - A survey on Vision-Language-Action (VLA) models highlights their significance in autonomous driving and human motor learning, discussing progress, challenges, and future trends [2][3][8] - The exploration of VLA models emphasizes their applications in embodied AI, showcasing a variety of datasets and methodologies [5][8][9] Group 2: Robotics and Reinforcement Learning - Research on foundation models in robotics addresses applications, challenges, and future directions, indicating a growing interest in integrating AI with robotic systems [3][4] - Deep reinforcement learning is identified as a key area with real-world successes, suggesting its potential for enhancing robotic capabilities [3][4] Group 3: Multimodal and Generative Approaches - The article discusses multimodal fusion and vision-language models, which are crucial for improving robot vision and interaction with the environment [6][8] - Generative artificial intelligence in robotic manipulation is highlighted as an emerging field, indicating a shift towards more sophisticated AI-driven solutions [6][8] Group 4: Datasets and Community Engagement - The article encourages engagement with a community focused on embodied intelligence, offering access to a wealth of resources, including datasets and collaborative projects [9]

Core Insights - The article discusses the potential of Vision-Language-Action (VLA) models in enhancing robotic operations through the integration of image generation and action prediction, highlighting the limitations of existing methods in forming a closed-loop perception-prediction-action cycle [3][16] - DreamVLA is introduced as a model that predicts comprehensive world knowledge to improve robotic performance, focusing on dynamic areas, depth perception, and high-level semantic features [4][5][16] Research Background and Motivation - Current VLA models are limited by image-based predictions, leading to information redundancy and a lack of critical world knowledge such as dynamics, spatial, and semantic understanding [3] - DreamVLA aims to construct a more effective perception-prediction-action loop by predicting comprehensive world knowledge, thereby enhancing the interaction between robots and their environment [3] Model Design Core Ideas - DreamVLA focuses on three core features: dynamic area prediction, depth perception, and high-level semantic features, which are essential for task execution [4][5] - Dynamic area prediction utilizes optical flow models to identify moving regions in a scene, optimizing the model's focus on task-critical areas [4] - Depth perception is achieved through depth estimation algorithms, providing 3D spatial context, while high-level semantic features are integrated from various visual models to enhance future state understanding [5] Structural Attention and Action Generation - A block structural attention mechanism is employed to separate queries into dynamic, depth, and semantic sub-queries, preventing cross-type knowledge leakage and maintaining clear representations [6] - The diffusion Transformer decoder is used to separate action representations from shared latent features, transforming Gaussian noise into action sequences through iterative self-attention and denoising processes [8] Experimental Results and Analysis - In benchmark tests, DreamVLA achieved an average task length of 4.44, outperforming other methods such as RoboVLM and Seer [9][10] - Real-world experiments with the Franka Panda robotic arm showed an average success rate of 76.7%, significantly higher than baseline models [10] Ablation Study Insights - The contribution of different knowledge types was analyzed, revealing that dynamic area prediction provided the most significant performance gain, while depth and semantic cues offered smaller, yet valuable, improvements [11] - Predicting future knowledge outperformed merely reconstructing current information, indicating that prediction provides better guidance for actions [12] - The block structural attention mechanism improved average task length from 3.75 to 4.44, demonstrating its effectiveness in reducing cross-signal interference [13] Core Contributions and Limitations - DreamVLA reconfigures VLA models into a perception-prediction-action framework, providing comprehensive foresight for planning through the prediction of dynamic, spatial, and high-level semantic information [16] - The model is currently limited to parallel gripper operations and relies on RGB data, with plans to incorporate more diverse data types and enhance generalization and robustness in future developments [15][16]

Core Viewpoint - The article discusses the development of a new model called CEED-VLA, which significantly enhances the inference speed of visual-language-action models while maintaining operational performance, making it suitable for high-frequency dexterous tasks [2][30]. Group 1: Model Development - The CEED-VLA model is designed to accelerate inference through a general method that improves performance across multiple tasks [2]. - The model incorporates a consistency distillation mechanism and mixed-label supervision to enable accurate predictions of high-quality actions from various intermediate states [2][6]. - The Early-exit Decoding strategy is introduced to address inefficiencies in the Jacobi decoding process, achieving up to 4.1× inference speedup and over 4.3× execution frequency [2][15]. Group 2: Experimental Results - Simulations and real-world experiments demonstrate that CEED-VLA significantly improves inference efficiency while maintaining similar task success rates [6][30]. - The model shows a speedup of 2.00× compared to the teacher model and achieves a higher number of fixed tokens, indicating improved performance [19][20]. - In real-world evaluations, CEED-VLA successfully completes dexterous tasks, achieving a success rate exceeding 70% due to enhanced inference speed and control frequency [30][31].

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Core Viewpoint - The article discusses the rapid advancements in embodied intelligence, highlighting its potential to revolutionize various industries such as manufacturing, healthcare, and space exploration through robots that can understand language, navigate complex environments, and make intelligent decisions [1]. Group 1: Embodied Intelligence Technology - Embodied intelligence aims to integrate AI systems with physical capabilities, allowing them to perceive and interact with the physical world [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in this transformative field [1]. - The core challenge in achieving true embodied intelligence lies in the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2]. Group 2: Role of MuJoCo - MuJoCo (Multi-Joint dynamics with Contact) is identified as a critical technology for embodied intelligence, serving as a high-fidelity simulation engine that bridges the virtual and real worlds [3]. - It allows researchers to conduct millions of trials in a simulated environment, significantly speeding up the learning process while minimizing hardware damage risks [5]. - MuJoCo's advantages include advanced contact dynamics algorithms, high parallel computation capabilities, and a variety of sensor models, making it a standard tool in both academia and industry [5][7]. Group 3: Practical Applications and Learning - A comprehensive MuJoCo development course has been created, focusing on practical applications and theoretical foundations within the embodied intelligence technology stack [9]. - The course includes project-driven learning, covering topics from physical simulation principles to deep reinforcement learning and Sim-to-Real transfer techniques [9][10]. - Participants will engage in six progressively complex projects, enhancing their understanding of robot control, perception, and collaborative systems [16][21]. Group 4: Course Structure and Target Audience - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of key technical points [13][17]. - It is designed for individuals with programming or algorithm backgrounds, graduate and undergraduate students focusing on robotics or reinforcement learning, and those interested in transitioning to the field of embodied robotics [28].

最近有社招的同学面到了HR环节，最终因为变现不是很出色被筛下来了，很可惜！今天，我们不谈技术，就 分享下在面试过程中，HR这个环节应该怎么面？ HR最想考察的是什么？ 我们沟通下来，HR最看重的无外乎以下几点： hr 最想要的人就是：稳定，忠诚，容易合作，善于沟通！ 态度良好，负责。 HR面试常问的问题有哪些？ 1）沟通，综合能力判断： 请做一个简单的自我介绍。关键点：谦逊，自信，建议总分结构，逻辑清晰，优势突出。 介绍一下你的优点和缺点。 关键点： 真诚，谦虚，不要过多，褒义中带贬义，沟通上还需加强，技术上爱钻 牛角尖等。 2）稳定性类问题： 你为什么离开上家公司。关键点： 不说不稳定，不要仇视上家公司，从客观的原因分析，最好是被动的。 找工作看中的点。关键点： 往应聘公司特点上靠，成长，机会。 为什么要来我们公司。关键点：结合招聘公司的实际情况，成长收获，看好贵司。 3）沟通&态度类问题： 如果和主管有冲突或者意见，当如何处理？关键点：可以多从自己身上找原因，每个人看问题视角不一样，主 管可能更多注整体和全局。 1）稳定性：工作稳定，工作负责（不要1年一次跳槽，你就是能力再强，也不敢要） 2）思维上：逻辑 ...

最近和几家公司对接了下，有一些大模型、强化学习、导航相关的职位需求，这里也和大家分享 下。职位比较靠谱，是具身领域的独角兽公司，资金充裕，感兴趣的同学可以底部扫码了解更多。 1）多模态大模型 base：北京、深圳 薪资：40k-80k/月 2.具有机器人感知/导航/操作、AI大语言模型/多模态大模型等领域丰富的从业经验； 3.了解具身智能领域前沿的VLM/VLN/VLA多模态模型算法，对于比较有挑战性的实际问题有自己的 判断和分析解决能力； 4.具有NaVid/MobilityVLA等将多模态大模型技术应用于机器人导航领域的算法研发及落地经验者优 先； 5.扎实的前沿算法研发与高效的工程实现能力，具备技术快速落地的能力； 方向：移动操作、导航、VLA等； 职位描述： 1.从事具身智能多模态大模型前沿算法研发，应用于室内外多个场景的移动操作平台。包括但不限于 具身智能大模型的框架设计、模型优化、面向导航和操作等下游任务的训练和部署等； 2.探索并推动大语言模型和多模态大模型在机器人领域的技术和Demo。 职位要求： 1.计算机科学、人工智能、机器人、控制工程等相关专业硕士及以上学历； 6.具有良好的团队合作能力 ...

Core Viewpoint - The discussion focuses on the concept of remote operation (遥操作) in the context of embodied intelligence, exploring its significance, advancements, and future potential in robotics and human-machine interaction [2][15][66]. Group 1: Definition and Importance of Remote Operation - Remote operation is not a new concept; it has historical roots in military and aerospace applications, but its relevance has surged with the rise of embodied intelligence [5][15]. - The emergence of embodied intelligence has made remote operation crucial for data collection and human-robot interaction, transforming it into a mainstream approach [17][66]. - The concept of remote operation is evolving, with discussions on how it can enhance human capabilities and provide a more intuitive interface for controlling robots [15][66]. Group 2: Experiences and Challenges in Remote Operation - Various types of remote operation experiences were shared, including surgical robots and remote-controlled excavators, highlighting the diversity of applications [6][21]. - The challenges of remote operation include latency issues, the complexity of control, and the need for intuitive human-machine interfaces [34][69]. - The discussion emphasized the importance of minimizing latency in remote operation systems to enhance user experience and operational efficiency [34][56]. Group 3: Future Directions and Innovations - The future of remote operation may involve a combination of virtual and physical solutions, such as using exoskeletons for realistic feedback and pure visual systems for ease of use [38][40]. - Innovations like the ALOHA system are prompting the industry to rethink robot design and operational frameworks, potentially leading to significant advancements in remote operation technology [103][106]. - The integration of brain-machine interfaces could represent the ultimate solution for overcoming current limitations in remote operation, allowing for seamless communication between humans and machines [37][99].