Group 1 - The core viewpoint of the article is that the company is recruiting for editorial, operational, and sales positions to support its growth during an upward phase [1][2][3][4]. Group 2 - The editorial position involves content creation and editing for the company's public account, requiring candidates to have a professional background and experience in content creation on platforms like Zhihu and WeChat [2]. - The sales position focuses on promoting the company's courses and hardware products, with a preference for candidates who have a sales background and an understanding of user needs and market dynamics [3]. - The operational role is responsible for managing the company's public account, Xiaohongshu, and community engagement, aiming to enhance follower loyalty and attention, requiring knowledge of self-media platform strategies [4].

Core Insights - The article discusses advancements in Vision-Language-Action (VLA) models and their integration with reinforcement learning (RL) techniques, highlighting various research papers and projects that contribute to this field [2][4][5]. Group 1: Offline RL-VLA - NORA-1.5 is introduced as a vision-language-action model trained using world model- and action-based preference rewards, showcasing its potential in offline reinforcement learning [2][4]. - The paper "Balancing Signal and Variance: Adaptive Offline RL Post-Training for VLA Flow Models" emphasizes the importance of balancing signal and variance in offline RL applications [7]. - CO-RFT presents an efficient fine-tuning method for VLA models through chunked offline reinforcement learning, indicating a trend towards optimizing model performance post-training [9]. Group 2: Online RL-VLA - The concept of reinforcing action policies by prophesying is explored, suggesting a novel approach to enhance online reinforcement learning for VLA models [22]. - WMPO focuses on world model-based policy optimization for VLA models, indicating a shift towards utilizing world models for better policy learning [24]. - RobustVLA emphasizes robustness-aware reinforcement post-training, highlighting the need for models to maintain performance under varying conditions [27]. Group 3: Hybrid Approaches - GR-RL aims to improve dexterity and precision in long-horizon robotic manipulation by combining offline and online reinforcement learning strategies [100]. - The paper "Discover, Learn, and Reinforce" discusses scaling VLA pretraining with diverse RL-generated trajectories, indicating a comprehensive approach to model training [104]. - SRPO introduces self-referential policy optimization for VLA models, showcasing innovative methods to enhance model adaptability and performance [106].

Core Viewpoint - The article emphasizes the importance of collaboration in the fields of embodied VLA+RL, robotic control, and data collection, highlighting the potential for valuable insights and projects in these areas [2]. Group 1: Collaboration Opportunities - The company seeks to recruit partners for the development of courses or practical projects related to embodied VLA+RL, robotic control, and data collection [2][4]. - There is an invitation for experienced individuals in the field to contribute, with a minimum requirement of having published at least one paper in a CCF-A level conference or having over one year of industry experience [5]. Group 2: Compensation and Engagement - The company offers above-industry-level compensation and resource sharing, with opportunities for part-time engagement [6]. - Interested parties are encouraged to reach out via WeChat for further discussions [3][6].

Core Viewpoint - The article discusses the challenges and complexities faced by beginners in implementing VLA (Vision-Language Alignment) models, emphasizing the need for practical experience and effective training methods to achieve successful deployment in real-world applications [2][4]. Group 1: Challenges in VLA Implementation - Many students report difficulties in achieving effective results with open-source models like GR00T and PI0, despite low training loss in simulations [2][4]. - The transition from simulation to real-world application (sim2real) poses significant challenges, particularly in data collection and model training [6][7]. - Beginners often struggle with the intricacies of VLA models, leading to prolonged periods of trial and error without achieving satisfactory outcomes [4][6]. Group 2: VLA Model Components - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with a focus on high-quality data acquisition [6]. - Training VLA models typically requires extensive simulation debugging, especially when real-world data is insufficient, utilizing frameworks like Mujoco and Isaac Gym [7]. - Post-training, models often require optimization techniques such as quantization and distillation to reduce parameter size while maintaining performance [9]. Group 3: Educational Initiatives - The article introduces a practical course aimed at addressing the learning curve associated with VLA technologies, developed in collaboration with industry experts [10][12]. - The course covers a comprehensive range of topics, including hardware, data collection, VLA algorithms, and real-world experiments, designed to enhance practical skills [12][25]. - The course is targeted at individuals seeking to enter or advance in the field of embodied intelligence, with prerequisites including a foundational knowledge of Python and PyTorch [22].

Core Insights - The article discusses the ViVLA framework, which enables robots to learn new skills from single video demonstrations, addressing the limitations of existing Vision-Language-Action (VLA) models in generalizing to tasks outside their training distribution [1][2][25] Group 1: Challenges in Robot Skill Generalization - Four core challenges hinder the generalization of robot skills: insufficient fine-grained action recognition, differences in action representation and modalities, inherent flaws in autoregressive modeling, and a lack of diverse expert-agent pairing data [4][5][7] Group 2: ViVLA's Technical Framework - ViVLA employs a three-layer technical system: unified action space construction, parallel decoding optimization, and large-scale data generation to achieve efficient learning from single video demonstrations [8] - The first layer focuses on latent action learning through an Action-Centric Cycle-Consistency (A3C) framework to bridge the gap between different expert and agent action spaces [10] - The second layer enhances model training efficiency with parallel decoding and spatiotemporal masking strategies, improving video understanding and reducing inference delays [11][12] Group 3: Data Generation and Validation - ViVLA's data generation pipeline converts human videos into high-quality paired data, resulting in a dataset of over 892,911 expert-agent training samples [13][17] - The framework's effectiveness is validated through a three-tier performance verification system, demonstrating significant improvements in unseen task success rates compared to baseline models [14][16] Group 4: Performance Metrics - In the LIBERO benchmark, ViVLA achieved over a 30% performance increase in unseen tasks compared to baseline models, with success rates of 74% in real-world manipulation tasks, significantly outperforming other models [14][16][18] - The model maintained a success rate of over 70% in varying environmental conditions, showcasing its robustness [20] Group 5: Future Directions and Limitations - While ViVLA represents a breakthrough in single-sample video imitation learning, there are areas for optimization, including enhancing error recovery capabilities and expanding data diversity through automated filtering of human videos [25][27]

Core Insights - The article discusses the advancements in embodied intelligence, particularly focusing on the VLA (Vision-Language-Action) model and its integration with reinforcement learning (RL) to enhance robotic capabilities [2][4][50]. Group 1: Importance of VLA and RL - VLA models are crucial for applying powerful visual-language models in robotic control, moving beyond mere imitation learning to achieve robust performance in novel situations [6][8]. - Traditional imitation learning is limited, as robots struggle in unfamiliar scenarios, necessitating the use of RL for continuous improvement through trial and error [8][12]. Group 2: Challenges in Applying RL to VLA - There are three main challenges in applying RL to VLA: environmental differences, model instability, and computational demands [12][13]. - Directly applying RL to large VLA models can lead to catastrophic forgetting and training collapse, making it difficult to maintain performance [12][13]. Group 3: iRe-VLA Model Design - The iRe-VLA model features a two-stage iterative learning process, combining exploration through online RL and consolidation via supervised learning [16][21]. - The architecture includes a VLM backbone for understanding and an Action Head for executing control signals, optimized using LoRA technology to reduce computational load [17][18]. Group 4: Experimental Results - Experiments in simulated environments (MetaWorld, Franka Kitchen) and real-world scenarios demonstrated that iRe-VLA significantly outperformed traditional methods, with success rates improving from 43% to 83% in certain tasks [38][39]. - In real-world applications, the model's success rate for grasping previously unseen objects increased from 35% to 80% after training, showcasing its enhanced generalization capabilities [40][43]. Group 5: Conclusion and Future Directions - The iRe-VLA approach presents a viable solution for deploying large models in robotic control, highlighting the potential for ongoing research in efficient exploration and stable RL algorithms [48][50]. - The model's design allows for effective resource allocation, with local robots handling lightweight tasks while cloud servers manage heavier computations, aligning with practical deployment scenarios [54].

Core Insights - The article introduces a specialized guidance program for academic papers in the field of embodied intelligence, highlighting the expertise of the faculty involved [1] - The program supports various research directions including large models, reinforcement learning, and robotics, catering to a wide range of academic needs [1] Group 1: Services Offered - The program provides comprehensive support for the entire paper process, including experimental guidance and doctoral application assistance [4] - There is a high success rate for papers already accepted in top conferences and journals such as CVPR, AAAI, and ICLR [4] Group 2: Target Publications - The guidance covers submissions to top-tier conferences and journals classified under CCF-A, CCF-B, CCF-C, as well as SCI and EI indexed publications [5] - It also includes support for various academic requirements such as thesis papers and competition entries [5]

Core Insights - Morgan Stanley predicts that 25 humanoid robot companies will dominate the industry, with 7 Chinese companies listed [2][3] - The Chinese companies include Baidu, Alibaba, Horizon Robotics, Junsheng Electronics, iFlytek, Desay SV, and Hesai Technology, focusing on various sectors such as AI, automotive, and electronic manufacturing [3][4] - The report emphasizes the importance of component and module suppliers over traditional humanoid robot manufacturers, highlighting the critical role of companies providing AI chips, visual sensors, precision actuators, and power management chips [3][4] Company and Industry Summary - The 7 Chinese companies identified are significant players in their respective fields, with a focus on AI, automotive intelligence, language recognition, and electronic manufacturing [3] - The absence of companies like Yushun and Zhiyuan in the report raised questions about its professionalism, but Morgan Stanley justified this by focusing on the foundational components essential for the humanoid robot industry [4] - The Chinese market has seen the emergence of nearly 150 humanoid robot startups, indicating a growing interest and investment in this sector, regardless of potential market bubbles [4]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Minghao Guo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、研究背景与核心动机 视觉-语言-动作（VLA）模型是具身智能领域的关键技术，能够让机器人直接从视觉观测和自然语言指令中生成控制动作。现有VLA模型大多依赖CLIP、SigLIP等 2D视觉编码器，这类编码器擅长捕捉图像与文本的语义对应关系，却无法编码3D空间信息（如深度、物体位姿、空间关系）。 这种缺陷会导致模型在操作任务中出现错误的注意力分配，如figure1所示：在"将桌布从桌角移到桌边"和"拾取盘子与ramekin之间的黑碗并放到盘子上"任务中，传 统VLA模型会错误关注无关区域，无法精准定位任务相关物体，进而影响操作任务的完成精度。 为解决这一问题，研究团队提出GLaD框架，核心思路是通过知识蒸馏将3D几何先验注入VLA模型，使其同时具备语义理解和空间推理能力，且无需依赖额外的深 度传感器或3D标注。 ...

Core Insights - Waymo has released a deep blog detailing its AI strategy centered around its foundational model, emphasizing the use of distillation methods to create high-efficiency models for onboard operations [1][2] - Jeff Dean highlighted the significance of knowledge distillation, comparing it to the creation of the Gemini Flash model, which showcases the importance of distillation in AI model efficiency [1][2] Historical Context of Rejected Papers - Many foundational technologies in AI, such as optimizers for large models and computer vision techniques, were initially rejected by top conferences, showcasing a historical pattern of oversight in recognizing groundbreaking innovations [6] - Notable figures in AI, including Geoffrey Hinton and Yann LeCun, have faced rejection for their pioneering work, which was later recognized as transformative [6] Case Studies of Rejected Innovations - LSTM, a milestone for sequence data processing, was rejected by NIPS in 1996 but later became crucial in speech recognition and machine translation, highlighting the delayed recognition of its value [7][10] - SIFT, a dominant algorithm in computer vision, faced rejection from ICCV and CVPR due to its perceived complexity, yet proved to be vital in real-world image processing [11][13] - Dropout, a key regularization method for deep neural networks, was initially rejected for its radical approach but later became essential in training deep networks effectively [17][19] - Word2Vec, despite being rejected at ICLR, became a cornerstone in NLP due to its efficiency and practical application, eventually receiving recognition for its impact [20][24] - YOLO transformed object detection by prioritizing speed over precision, facing rejection for its perceived shortcomings but later becoming a widely adopted framework in the industry [28][30] Reflection on Peer Review Limitations - The peer review system often struggles to recognize disruptive innovations, leading to a systematic cognitive lag in evaluating groundbreaking research [40][41] - The tendency to equate mathematical complexity with research contribution can hinder the acceptance of simpler yet effective methods [41] - Historical examples illustrate that the true measure of a research's impact is not determined by initial peer review outcomes but by its long-term relevance and problem-solving capabilities [43][47]