点击下方 卡片 ，关注" 具身智能 之心 "公众号 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。 最近看到了很多HR的职位需求，list很长，但VLA算法是很"急需"。尤其是想挖自驾这方面的人才。这一点，也体 现在论文数量上。每天带着很多小朋友看论文，也几乎都和VLA"挂钩"。但自驾VLA和具身VLA，确实有很多相通 的地方，但底层差距还是很大的。。。。 但VLA貌似"很伤"，不好调，数据采集麻烦。这个事情，是很多同学持续在吐槽的。 只看论文而没有真机实验，在仿真里面做了好久，也不知道动起来啥样子。确实，具身和其它领域都有所不同，太 注重本体，即使是算法也极其依赖硬件。 不少同学说，相当多的时间"浪费"在踩坑上了。 确实，真实数据采集上，需要借助硬件完成，比如遥操、VR、全身动补等方式。仿真和互联网数据，在泛化性能上 依然得不到保证，很多具身公司坚持"真机数据"路线。但真机数据采的数据并不好用，该怎么办？一轮下来又需要 好久。 模型优化上也很 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 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。 最近看到了很多HR的职位需求，list很长，但VLA算法是很"急需"。这一点，也体现在论文数量上。每天带着很多 小朋友看论文，也几乎都和VLA"挂钩"。 ❝ 但VLA貌似"很伤"，不好调，数据采集麻烦。这个事情，是很多同学持续在吐槽的。 只看论文而没有真机实验，在仿真里面做了好久，也不知道动起来啥样子。确实，具身和其它领域都有所不同，太 注重本体，即使是算法也极其依赖硬件。 ❝ 不少同学说，相当多的时间"浪费"在踩坑上了。 确实，真实数据采集上，需要借助硬件完成，比如遥操、VR、全身动补等方式。仿真和互联网数据，在泛化性能上 依然得不到保证，很多具身公司坚持"真机数据"路线。但真机数据采的数据并不好用，该怎么办？一轮下来又需要 好久。 模型优化上也很难顶，有的效果就是调不出，或者说训练不出效果。有些算法就是没效果，不知道怎么分析，真机 上一塌糊涂 ...

Core Viewpoint - The article emphasizes the increasing demand for VLA (Vision-Language Alignment) algorithms in the industry, highlighting the challenges faced by practitioners in data collection and model optimization [2][4]. Group 1: Industry Demand and Challenges - There is a significant demand for VLA algorithms, as reflected in the numerous job postings and research papers related to this field [2]. - Practitioners often face difficulties with VLA due to complex data collection processes and the reliance on hardware, leading to frustrations about wasted time and ineffective model training [2][4]. - Many companies in the embodied intelligence sector are committed to using real machine data, but the quality of this data can be suboptimal, complicating the training process [2][4]. Group 2: Educational Initiatives - The article introduces a practical course aimed at addressing the learning curve associated with VLA, developed in collaboration with industry experts [5]. - The course covers a comprehensive curriculum, including hardware, data collection, VLA algorithms, and real-world applications, designed to facilitate effective learning [8][9]. - Participants in the course will receive a SO-100 robotic arm, enhancing hands-on experience and practical application of the learned concepts [9]. Group 3: Course Structure and Content - The course is structured into nine chapters, covering topics from VLA basics to advanced model deployment and evaluation [11][12][13][14][15][16][17][18]. - Key areas of focus include data acquisition, model training, simulation environments, and the integration of VLA with world models [8][9][11][12][13][14][15][16][17]. - The course aims to equip learners with the necessary skills to transition into roles as algorithm engineers with 1-2 years of experience upon completion [25].

Core Viewpoint - The article emphasizes the increasing demand for VLA (Variable Latent Action) algorithms in the industry, highlighting the challenges associated with data collection and model training, which are critical for successful implementation in real-world applications [1][2][3]. Group 1: VLA Algorithm Demand and Challenges - There is a significant demand for VLA algorithms, as evidenced by numerous job postings and the increasing number of related research papers [1]. - Many practitioners express frustration over the difficulties in tuning VLA algorithms and the complexities involved in data collection [2]. - The reliance on real machine data for effective VLA model training poses challenges, as the data collected often proves to be inadequate for practical applications [3][8]. Group 2: Data Collection and Training - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with a focus on remote operation and VR technologies [10]. - Effective data collection and ensuring high-quality data are crucial, particularly in the context of real-to-sim-to-real (real2sim2real) methodologies [10]. - Training VLA models typically requires simulation debugging, especially when real machine data is insufficient, with frameworks like Mujoco and Isaac Gym being essential for this process [11]. Group 3: Model Deployment and Optimization - After training, VLA models often require optimization techniques such as quantization and distillation to reduce parameter size while maintaining performance [12]. - The deployment of VLA models on edge devices presents challenges due to their large parameter sizes, necessitating lightweight operations [12]. - The article discusses the importance of fine-tuning models and the various tricks involved in training complex models like π0 and π0.5, which require significant expertise [11][8]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn about VLA, covering topics such as hardware, data collection, algorithm training, and model deployment [13][17]. - The course is designed to address the rapid advancements in VLA technology and aims to equip participants with hands-on experience and knowledge [13][18]. - It includes a comprehensive curriculum that spans various aspects of VLA, from foundational concepts to advanced deployment techniques [19][20][21].

Core Viewpoint - The article discusses the rapid development and challenges of the VLA (Whole Body Visual Learning Algorithm) in the field of embodied intelligence, highlighting the importance of real data collection and the difficulties faced by newcomers in the field [2][3][4]. Group 1: VLA Development and Challenges - The VLA algorithm is experiencing explosive growth, with various frameworks and tools, such as reinforcement learning (RL), enhancing its performance [2]. - Data collection methods are diversifying, with millions of open-source data becoming available, indicating a potential for industrialization [2]. - Many practitioners express frustration with the challenges of tuning VLA models and the complexities of data collection, particularly for those new to the field [3][5]. Group 2: Data Collection and Training - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with a focus on remote operation and VR for mechanical arms [13]. - Simulation and real-to-sim-to-real (real2sim2real) techniques are crucial for training VLA models, especially when real data is insufficient [14]. - Training techniques are critical, with many practitioners struggling to achieve good results due to the complexity of models like π0 and π0.5, which require high attention to detail [14][10]. Group 3: Model Deployment - After training, VLA models require optimization to reduce their parameter size for deployment, which is essential for edge computing applications [15]. - Techniques such as quantization and distillation are necessary to maintain performance while minimizing model size [15]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn VLA effectively, covering hardware, data collection, algorithm deployment, and real-world experiments [17][20]. - The course is designed to save time and reduce the learning curve for newcomers, providing practical experience that can enhance resumes [18][31].

Core Viewpoint - The article discusses the rapid growth and potential of VLA (Whole Body Visual Learning) algorithms in the field of embodied intelligence, highlighting the increasing availability of diverse data sources and standardized evaluation metrics, which may lead to industrialization soon [2][12]. Group 1: VLA Development and Challenges - VLA algorithms are experiencing explosive growth, supported by various frameworks and tools like reinforcement learning (RL) that enhance their generalization performance [2]. - Despite the promising direction, many practitioners face challenges with VLA, including difficulties in tuning and data collection, leading to frustrations among newcomers in the field [3][10]. - Real data collection is essential, often requiring hardware setups such as remote operation and VR, but the quality of real-world data can be suboptimal, complicating the training process [5][11]. Group 2: VLA Implementation Modules - The implementation of VLA involves several key modules, including data collection methods based on imitation learning and reinforcement learning, with a focus on ensuring high-quality data [13]. - Training VLA models typically requires simulation debugging, especially when real-world data is insufficient, with frameworks like Mujoco and Isaac Gym being crucial for this process [14]. - After training, VLA models need to undergo a "slimming" process to reduce parameter size for deployment, which involves techniques like quantization and distillation to maintain performance while minimizing resource usage [15]. Group 3: Educational Initiatives - To address the learning curve associated with VLA technologies, a specialized course has been developed, focusing on practical skills and project experience in the field of embodied intelligence [16][19]. - The course covers a comprehensive curriculum, including hardware, data collection, VLA algorithms, evaluation, simulation, and real-world experiments, aimed at equipping participants with the necessary skills for the industry [21][36].

Core Viewpoint - The article emphasizes the increasing demand for VLA (Variable Learning Algorithm) in the industry, highlighting the challenges associated with data collection and model training, and the need for practical learning resources in this field [1][2][3]. Group 1: VLA Demand and Challenges - There is a significant demand for VLA algorithms in job postings, indicating a growing interest in this technology [1]. - Many practitioners express frustration with the difficulties in tuning VLA algorithms and the complexities of data collection [2]. - The reliance on real machine data for effective VLA model training poses challenges, as many companies struggle with the quality of the collected data [3]. Group 2: VLA Implementation Modules - The implementation of VLA involves several key modules, including data collection methods based on imitation learning and reinforcement learning [8]. - Training VLA models typically requires simulation debugging, especially when real machine data is insufficient, making simulation frameworks like Mujoco and Isaac Gym crucial [9]. - After training, VLA models often require optimization techniques such as quantization and distillation to reduce model size while maintaining performance [10]. Group 3: Educational Resources and Courses - The article introduces a practical course aimed at helping individuals learn VLA effectively, addressing the rapid updates in technology and the challenges faced by learners [11]. - The course covers a comprehensive curriculum, including mechanical arm hardware, data collection, VLA algorithms, evaluation, simulation, and deployment [16][17]. - Participants will receive hands-on experience with real hardware, enhancing their learning and practical skills in the VLA domain [28].

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) models, emphasizing the importance of real machine data and practical experience in achieving effective results in embodied intelligence applications [2][4]. Group 1: Data Collection - Data collection methods primarily include imitation learning and reinforcement learning, with remote operation, VR, and full-body motion capture being key techniques [6][7]. - Ensuring high-quality data and effective data collection is crucial, particularly in the context of sim2real applications [7]. Group 2: VLA Training - Prior to real machine deployment, simulation debugging is essential, especially when real machine data is insufficient, making frameworks like Mujoco and Isaac Gym important [9]. - Training techniques are critical, with challenges in fine-tuning models and achieving good results with small data sets; models like π0 and π0.5 require high attention to detail and experience [9][10]. Group 3: VLA Model Deployment - After training, models need to undergo a "slimming" process due to their typically large parameter sizes, which poses challenges for deployment on edge chips; techniques like quantization and distillation are necessary [11]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping students effectively learn VLA, covering various aspects such as hardware, data collection, algorithms, evaluation, simulation, and deployment [12][14]. - The course is designed for individuals seeking to enter the embodied intelligence field, including students and professionals transitioning from traditional CV, robotics, or autonomous driving sectors [24].

Core Viewpoint - The article discusses the implementation challenges and advancements of VLA (Variable Latent Action) algorithms and Reinforcement Learning (RL) in robotics, focusing on their practical applications and future developments in the field of embodied intelligence [3][13]. Group 1: Guest Speakers - Wei Sui, Vice President of Diguo Robotics, has extensive experience in developing 2.5D and 3D vision algorithms for robotics and autonomous driving, leading a team that created a comprehensive 4D labeling system, with millions of chips shipped [5]. - Zhang Qiang, Chief Researcher and Academic Committee Director at Beijing Humanoid Robotics, specializes in humanoid robot motion control and multimodal perception, contributing to the development of core RL algorithms for humanoid robots [6][8]. - Wang Tiancai, Partner at Yuanli Lingji, has published over 30 papers in top international conferences and is a core author of notable algorithms in end-to-end autonomous driving [9][10]. - Yu Chao, Assistant Professor at Tsinghua Shenzhen Research Institute, focuses on decision intelligence driven by reinforcement learning, with over 50 published papers and significant academic recognition [11][12]. Group 2: Key Topics Discussed - The article addresses the pain points in the architecture and models of VLA, exploring how to enhance the overall motion control of robots [16]. - It discusses the integration of VLA with RL for better real-world application, including considerations for hardware selection and lightweight implementations [16].

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data for effective model training and deployment, as well as the need for practical learning resources in this rapidly evolving area [2][4][14]. Group 1: Data Collection - Data collection methods in VLA primarily include imitation learning and reinforcement learning, with remote operation, VR, and full-body motion capture being key techniques [8]. - Ensuring high-quality data collection is crucial, and methods like real2sim2real are highlighted as important for effective data utilization [8]. Group 2: VLA Training - Before deploying models on real machines, simulation debugging is essential, especially when real machine data is insufficient, utilizing frameworks like Mujoco and Isaac Gym [10]. - Training techniques are critical, with challenges in fine-tuning models and achieving good results with limited data being common issues faced by learners [10][11]. - Some algorithms, such as ACT, are easier to train, while others like π0 and π0.5 require more intricate techniques and experience [11]. Group 3: VLA Deployment - After training, models often require optimization to reduce their size, as VLA models typically have large parameter counts, posing challenges for deployment on edge devices [13]. - Techniques such as quantization and distillation are necessary to minimize parameters while maintaining performance [13]. Group 4: Educational Resources - The article introduces a practical course aimed at helping learners effectively navigate the complexities of VLA, covering hardware, data collection, algorithms, and deployment [14][16]. - The course is designed for various audiences, including those seeking to enter the field, advance their skills, or transition from related areas like traditional computer vision or robotics [24].