随着视觉-语言-动作（VLA）模型取得重大突破，将其与强化学习（RL）相结合已成为极具前景的新范 式。该范式能充分发挥环境试错交互和预收集次优数据的双重优势。这里也为大家盘点下领域相关工作。 0）综述 点击下方 卡片 ，关注" 具身智能 之心 "公众号 What Can RL Bring to VLA Generalization? An Empirical Study （2025） 论文链接：https://arxiv.org/abs/2505.19789 1）无环境离线RL训练 MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models （ICRA2025） 论文链接：https://arxiv.org/abs/2503.08007 Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions （2023） 论文链接：https://arxiv.org/abs/2 ...

还在等导师"喂饭"？还在想"基础打好再发"？醒醒！科研开窍要趁早，拒信和延毕不会等你准备 好！ 看到"延毕"两个字，是不是心里一紧？每年，都有不少才华横溢的硕士，明明能力不差，却卡在 了"论文"这道坎上。不是不努力，而是"开窍"太晚！ "开窍"晚的典型画像 "等导师安排"型： 总觉得导师没给明确方向/任务，自己就无从下手。被动等待，时间悄然流逝。 "追求完美"型： 总想"学完所有知识"、"打好完美基础"、"做出惊天成果"再开始写。结果？基础 永远学不完，实验永远不完美。 "畏难拖延"型： 一想到读文献、调模型、写论文、被拒稿就头大，下意识逃避，用课程、项目甚 至游戏来麻痹自己。 "低估周期"型： 天真地以为写论文、投稿、修改、接收是几个月就能搞定的事情。殊不知，从idea 到接收，动辄半年到一年甚至更久！审稿被拒？周期再加倍！ 科研"开窍"的核心是什么？ 核心就四个字： 尽早行动！ 把"发论文"当成 贯穿硕士生涯 的核心目标，而非最后冲刺的任务。 算一笔"时间账"： 研一暑假开始投入：你有近2年时间打磨1-2篇高质量论文（含投稿周期），游刃有余。 研二下才开始着急：留给你的有效时间可能不足1年，还要面临课程、 ...

Core Viewpoint - GeoScan S1 is introduced as a high-performance, cost-effective handheld 3D laser scanner, designed for various applications with advanced features such as multi-sensor integration and real-time 3D reconstruction capabilities [1][3][4]. Product Introduction - GeoScan S1 features lightweight design, one-click operation, and centimeter-level precision for real-time 3D scene reconstruction. It can cover areas over 50,000 square meters and supports a measurement distance of up to 70 meters with a point cloud generation rate of 100,000 points per second [1][20][21]. - The device is equipped with a handheld Ubuntu system and various sensor devices, allowing for flexible integration and expansion for research and development [1]. Team Background - The product is developed through collaboration between Professor Liu Chun's team from Tongji University and the industrialization team from Northwestern Polytechnical University, backed by years of research and numerous validated projects [3]. Technical Specifications - The GeoScan S1 supports multi-sensor fusion, including RTK, 3D laser radar, dual wide-angle cameras, and a depth camera, achieving high precision and reliability in complex environments [8][12][23]. - It has a relative accuracy of better than 3 cm and absolute accuracy of better than 5 cm, with a maximum scanning area of 50,000 square meters and a point cloud output of 200,000 points per second [15][20]. Software Features - The software allows for data collection and storage in various formats, including .pcd and .bag files, and supports real-time mapping and color point cloud generation [28][29]. - Users can initiate RTK functionality and 3D Gaussian data collection, with options for online and offline versions available for enhanced capabilities [29][44]. Application Scenarios - GeoScan S1 is suitable for various environments, including office buildings, parking lots, industrial parks, tunnels, and forests, enabling precise 3D mapping [33]. - The device supports integration with unmanned platforms such as drones and robots, facilitating automated operations [31]. Pricing Information - The base version of GeoScan S1 is priced at 19,800, with additional versions available at higher price points for enhanced features [44].

Core Viewpoint - The article discusses the advancements in robotic models, particularly focusing on the development of the RT-2 and RT-X models, which enhance the capabilities of robots in executing tasks through visual language models and diverse datasets [5][10][11]. Group 1: RT-2 and Its Capabilities - RT-2 is introduced as a foundational robot model that can process visual questions and execute tasks based on language instructions, showcasing the potential of remote-accessible robotic models [5][7]. - The model's ability to convert robot control tasks into question-answer formats allows it to perform various basic language instructions effectively [7][8]. Group 2: RT-X Dataset and Its Impact - The RT-X dataset, developed by DeepMind, comprises data from 34 research labs and 22 types of robots, providing a diverse training ground for robotic models [10]. - Models trained on the RT-X dataset outperform specialized models by approximately 50% in various tasks, indicating the advantages of cross-embodiment models [11]. Group 3: Evolution of VLA Models - The first-generation VLA model, RT-2, is noted for its simplicity, while the second-generation models utilize continuous action distributions for improved performance in complex tasks [14][15]. - The second-generation VLA models incorporate specialized mechanisms for generating continuous actions, enhancing their control capabilities [17][18]. Group 4: π0 and π0.5 Models - The π0 model, based on a large language model with 3 billion parameters, is designed to handle various tasks, including folding clothes, demonstrating its adaptability in different environments [18][23]. - The latest π0.5 model is aimed at executing long-term tasks in new environments, integrating high-level reasoning capabilities to manage complex instructions [28][30]. Group 5: Future Directions and Reinforcement Learning - Future VLA models are expected to integrate reinforcement learning techniques to enhance robustness and performance, moving beyond imitation learning [34][39]. - The combination of VLA and DLA (Deep Learning Architecture) is proposed to create a more effective system, leveraging expert data to improve generalist capabilities [44][46].

Core Viewpoint - The article emphasizes the significance of legged robots in the field of robotics, highlighting their ability to navigate complex terrains and perform various tasks, making them a focal point for future applications in inspection, security, rescue, and industrial automation [2][4]. Group 1: Importance of Legged Robots - Legged robots are considered a milestone in robotics due to their capability to handle complex environments and obstacles, moving beyond flat surfaces [2]. - There is a growing demand for talent in the legged robotics sector, with companies willing to invest heavily in skilled individuals [2]. - The article suggests that now is the optimal time to enter the legged robotics field, as it presents numerous opportunities for learning and development [2]. Group 2: Educational Initiatives - The article introduces a comprehensive course titled "From Quadruped to Biped: Full-Stack Algorithms," aimed at addressing the challenges faced by beginners in the legged robotics domain [2]. - The course covers a full technology stack from quadruped to biped robots, incorporating real-world applications and simulation environments like Isaac Gym, Gazebo, and MuJoCo [2][4]. - Key topics include the basics of quadruped robots, advanced biped robot techniques, and high-level algorithms for multi-task adaptation [2][4]. Group 3: Technical Aspects - The curriculum includes kinematics and dynamics, multi-modal sensor fusion, and practical implementations in simulation environments [3][4]. - It also covers deep reinforcement learning and imitation learning techniques, focusing on algorithms like PPO and SAC for gait control [4]. - Safety mechanisms, collision detection, and hardware deployment strategies are integral parts of the training, ensuring a comprehensive understanding of real-world applications [4][7]. Group 4: Target Audience and Prerequisites - The course is designed for AI robotics practitioners, graduate and undergraduate students, career changers, and enthusiasts interested in cutting-edge technology [16]. - Participants are expected to have a foundational knowledge of programming, algorithms, and mathematics, with recommendations for having a GPU for practical exercises [16][17]. - The training emphasizes hands-on experience, allowing learners to translate theoretical knowledge into practical engineering solutions [16].

曾经囿于实验室精密操控的具身智能，如今正以惊人的态势突破感知与行动的重重壁垒，灵巧的机械臂 在无序环境中自主作业，智能体在复杂动态场景中理解并执行指令，机器人真正"理解"物理世界并与人 类自然协作......具身智能的落地应用，正从蓝图变为触手可及的现实，这不仅是技术的跃迁，更是一场 颠覆人机交互范式、重塑产业未来的场景革命！ 把握变革脉搏，共绘智能蓝图！ 2025年8月13-15日 ，万众瞩目的 2025中国具身智能机器人产业大会 暨展览会 将在 上海新国际博览中心 盛大启幕！ 来EAI SHOW 2025： 洞悉前沿趋势： 聚焦具身智能，聆听全球顶尖专家解读技术突破与产业方向，打通技术到商业的变现通路！ 见证创新力量： 近距离接触未来科技，体验最具突破性的具身智能产品与解决方案； 链接产业生态： 同期精彩活动云集，现场与产、学、研、投各界专业人士深度交流，探寻合作共赢新机遇。 [ 聚2焦0具2身5智能] 零距离接触未来科技 聚焦具身智能，一站式把握产业脉搏 。无论是知名企业还是行业新锐，产业链上中下游的企业、产品 和解决方案都将展示更多创新应用方向，为观众带来具身智能机器人创新成果与新兴产业深度融合的沉 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Bin Liu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。 核心观点与背景 本篇综述探讨了贝叶斯统计与具身智能之间深层次的概念联系。具身智能理论认为，认知能力从根本上源 于并受制于智能体与环境的实时传感器交互。这种适应性行为本质上需要在不确定性下进行持续推理。贝 叶斯统计为此提供了一个原则性的概率框架，通过将知识表示为概率分布，并根据新证据更新信念。 研究指出，尽管存在这种深层概念联系，贝叶斯原则在当今的具身智能系统中并未得到广泛应用。本文通 过两个关键视角分析这一现象：搜索和学习——这两个主题被Rich Sutton在著名文章"The Bitter Lesson"中 强调为现代AI的核心。 搜索与学习：现代AI的两个基础主题 Rich Sutton的"The Bitter Lesson"强调，搜索和学习代表了能够随着计算能力增加而驱动AI重大突破的通用 方法。搜索指系统地探索大 ...

Core Viewpoint - The article discusses the advancements in robotic models, particularly focusing on the development of the RT-2 and RT-X models, which enhance the capabilities of robots in executing complex tasks through improved data sets and model architectures [6][12][44]. Group 1: RT-2 and RT-X Models - RT-2 is introduced as a foundational robot model that utilizes a visual language model to process image-based commands and execute tasks [8][10]. - The RT-X dataset, developed by DeepMind, comprises data from 34 research labs and 22 types of robots, showcasing a diverse range of robotic capabilities [13][26]. - Cross-embodiment models trained on the RT-X dataset outperform specialized models by approximately 50% in various tasks, indicating the advantages of generalization in robotic learning [13][29]. Group 2: Evolution of VLA Models - The first generation of VLA models, like RT-2, is based on simple question-answer structures for robot control, while the second generation incorporates continuous action distributions for better performance [16][19]. - The second generation VLA models, such as π0, utilize a large language model with an action expert module to handle complex tasks, generating action sequences over time [22][24]. - The π0.5 model is designed for long-term tasks, integrating high-level reasoning to execute complex instructions in new environments [36][40]. Group 3: Integration of Reinforcement Learning - Future VLA models are expected to incorporate reinforcement learning techniques to enhance robustness and performance, moving beyond imitation learning [44][49]. - The integration of reinforcement learning with VLA aims to create a more effective training process, allowing robots to learn from both expert data and real-world interactions [56][60]. - Current research is focused on developing stable and effective end-to-end training processes that leverage reinforcement learning to improve VLA capabilities [60].

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。 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 我们正在筹备北京、上海、深圳、广州、杭州、武汉建立项目研发团队。可以兼职哦，承 ...

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